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| <html lang="ko"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Agentness Arena</title> | |
| <style> | |
| /* Agentness Arena — visual-only game; only meta-controls carry text/icons. */ | |
| * { box-sizing: border-box; } | |
| html, body { | |
| margin: 0; background: #0e0f13; color: #d8dae0; | |
| font: 14px/1.4 system-ui, sans-serif; | |
| } | |
| #app { max-width: 1100px; margin: 0 auto; padding: 14px; } | |
| #bar { | |
| display: flex; align-items: center; justify-content: space-between; | |
| gap: 12px; flex-wrap: wrap; | |
| } | |
| .brand { font-size: 18px; font-weight: 600; letter-spacing: .3px; } | |
| .controls { display: flex; align-items: center; gap: 8px; } | |
| .ctl { display: flex; align-items: center; gap: 4px; font-size: 16px; } | |
| select { | |
| background: #1a1c22; color: #d8dae0; border: 1px solid #333; | |
| border-radius: 6px; padding: 4px 6px; font-size: 13px; | |
| } | |
| #startBtn { | |
| background: #2a6df4; color: #fff; border: 0; border-radius: 6px; | |
| width: 34px; height: 30px; font-size: 15px; cursor: pointer; | |
| } | |
| #startBtn:hover { background: #3f7df6; } | |
| /* rule setup row (below the Player chooser) */ | |
| #setupBar { | |
| display: flex; align-items: center; gap: 10px; flex-wrap: wrap; | |
| margin: 10px 0; padding: 8px 12px; border-radius: 8px; | |
| background: #14161c; border: 1px solid #232733; | |
| } | |
| .setupLabel { font-size: 12px; color: #9aa0ac; font-weight: 600; } | |
| /* player chooser: human vs AI agent */ | |
| #playerMode { | |
| display: flex; align-items: center; gap: 8px; flex-wrap: wrap; | |
| margin: 12px 0; padding: 8px 12px; border-radius: 8px; | |
| background: #14161c; border: 1px solid #232733; | |
| } | |
| .pmLabel { font-size: 12px; color: #9aa0ac; font-weight: 600; } | |
| .pmOpt { | |
| display: inline-flex; align-items: center; gap: 5px; cursor: pointer; | |
| font-size: 13px; color: #c7cad2; padding: 4px 10px; | |
| border: 1px solid #2a2f3a; border-radius: 999px; background: #1a1c22; | |
| user-select: none; | |
| } | |
| .pmOpt:hover { border-color: #3a6df4; } | |
| .pmOpt:has(input:checked) { border-color: #2a6df4; background: #1f2a44; color: #fff; } | |
| .pmOpt input { accent-color: #2a6df4; } | |
| .pmHint { font-size: 11.5px; color: #8b93a3; margin-left: auto; } | |
| /* WHO-plays gate: hide the LLM chat panel unless the AI agent is chosen. */ | |
| #app[data-mode="human"] #llmPanel { display: none; } | |
| #steps { display: flex; gap: 8px; margin: 12px 0; } | |
| .step { | |
| flex: 1; text-align: center; padding: 6px; border-radius: 6px; | |
| background: #16181e; color: #777; font-size: 12px; border: 1px solid #222; | |
| } | |
| .step.on { background: #1f2a44; color: #cfe0ff; border-color: #2a6df4; } | |
| .step.done { color: #6fbf73; } | |
| /* always-visible per-stage instruction banner */ | |
| #stageGuide { | |
| margin: 10px 0; padding: 10px 14px; border-radius: 8px; | |
| background: #14161c; border: 1px solid #232733; | |
| border-left: 4px solid #3a4150; /* accent recoloured per stage below */ | |
| } | |
| .sgHead { display: flex; align-items: baseline; gap: 8px; flex-wrap: wrap; } | |
| .sgTag { | |
| font-size: 11px; font-weight: 700; letter-spacing: .4px; | |
| padding: 2px 7px; border-radius: 999px; | |
| background: #232733; color: #aab2c2; | |
| } | |
| .sgTitle { font-size: 14px; color: #eef1f6; } | |
| .sgBody { margin: 6px 0 0; font-size: 12.5px; line-height: 1.55; color: #b9c0cc; } | |
| .sgBody b { color: #e7ebf2; } | |
| /* stage-specific accent + tag colour (mirrors the #steps highlight palette) */ | |
| #stageGuide[data-stage="idle"] { border-left-color: #3a4150; } | |
| #stageGuide[data-stage="memory"] { border-left-color: #2a6df4; } | |
| #stageGuide[data-stage="memory"] .sgTag { background: #1f2a44; color: #cfe0ff; } | |
| #stageGuide[data-stage="live"] { border-left-color: #c9a23a; } | |
| #stageGuide[data-stage="live"] .sgTag { background: #2e2818; color: #f0d98a; } | |
| #stageGuide[data-stage="report"] { border-left-color: #6fbf73; } | |
| #stageGuide[data-stage="report"] .sgTag { background: #1d2a1e; color: #a8e0ab; } | |
| /* collapsible reference toggles row (legend + rule info) */ | |
| #toggles { display: flex; flex-direction: column; gap: 8px; margin-top: 10px; } | |
| #legend > summary { | |
| list-style: none; cursor: pointer; display: inline-block; | |
| background: #1a1c22; color: #c7cad2; border: 1px solid #2a2f3a; | |
| border-radius: 6px; padding: 5px 12px; font-size: 12px; | |
| } | |
| #legend > summary::-webkit-details-marker { display: none; } | |
| #legend > summary:hover { border-color: #3a6df4; color: #fff; } | |
| #legend[open] > summary { border-color: #3a6df4; color: #fff; margin-bottom: 8px; } | |
| .legendGrid { | |
| border: 1px solid #252932; border-radius: 8px; background: #0f1117; | |
| padding: 10px 12px; display: grid; grid-template-columns: 1fr 1fr; gap: 8px 16px; | |
| } | |
| .lgItem { display: flex; align-items: flex-start; gap: 8px; font-size: 12px; color: #b9c0cc; } | |
| .lgItem b { color: #e7ebf2; } | |
| .lgItem em { color: #cfe0ff; font-style: normal; } | |
| .lgSw { | |
| flex: 0 0 auto; width: 18px; height: 18px; margin-top: 1px; | |
| border-radius: 4px; border: 1px solid #2a2f3a; background: #222; | |
| } | |
| .lgSw.lgTok { background: radial-gradient(circle, #aab4c4 2px, rgba(150,170,200,0.15) 3px); } | |
| .lgSw.lgSacred { | |
| background: repeating-linear-gradient(45deg, #5a4fb0 0 1.5px, #15161b 1.5px 5px); | |
| } | |
| .lgSw.lgZone { background: #15161b; border: 2px dashed #3fa7ff; } | |
| .lgSw.lgNet { background: linear-gradient(90deg, #7fce97 0 58%, #e0594f 58% 100%); } | |
| .lgHead { | |
| font-size: 11px; font-weight: 600; color: #9aa0ac; | |
| margin-top: 4px; padding-top: 8px; border-top: 1px solid #252932; | |
| } | |
| .lgSw.lgViolate { background: #15161b; border: 2px solid #ff5050; } | |
| .lgSw.lgPred { | |
| background: #15161b; | |
| border: 2px solid #6fbf73; box-shadow: inset 0 0 0 1px #888; | |
| } | |
| @media (max-width: 640px) { .legendGrid { grid-template-columns: 1fr; } } | |
| main { display: flex; gap: 14px; align-items: flex-start; } | |
| canvas { | |
| background: #15161b; border-radius: 10px; display: block; | |
| image-rendering: crisp-edges; | |
| } | |
| #board { cursor: pointer; } | |
| #side { flex: 0 0 auto; } /* HUD bars panel (the canvas carries its own box) */ | |
| /* dedicated info column to the RIGHT of the HUD bars: legend + report explainer, | |
| each a compact toggle. Fixed width so it doesn't stretch the board row. */ | |
| #infoPanel { flex: 0 0 250px; display: flex; flex-direction: column; gap: 8px; } | |
| #infoPanel > details { width: 100%; } | |
| #infoPanel > details > summary { | |
| display: block; width: 100%; text-align: center; | |
| list-style: none; cursor: pointer; | |
| background: #1a1c22; color: #c7cad2; border: 1px solid #2a2f3a; | |
| border-radius: 6px; padding: 6px 10px; font-size: 12px; | |
| } | |
| #infoPanel > details > summary::-webkit-details-marker { display: none; } | |
| #infoPanel > details > summary:hover { border-color: #3a6df4; color: #fff; } | |
| #infoPanel > details[open] > summary { border-color: #3a6df4; color: #fff; margin-bottom: 8px; } | |
| /* both explainers render single-column to fit the narrow column */ | |
| #infoPanel .legendGrid, | |
| #infoPanel .rpGrid { grid-template-columns: 1fr; } | |
| #infoPanel .legendGrid { gap: 7px; padding: 9px 10px; } | |
| #infoPanel .lgItem, | |
| #infoPanel .rpItem { font-size: 11.5px; } | |
| #infoPanel #reportInfoBody { padding: 10px 11px; } | |
| #hint { | |
| margin-top: 12px; min-height: 20px; color: #9aa0ac; font-size: 13px; | |
| } | |
| /* rule & settings explainer (toggle) */ | |
| #ruleInfo { margin-top: 10px; } | |
| #ruleInfoToggle { | |
| background: #1a1c22; color: #c7cad2; border: 1px solid #2a2f3a; | |
| border-radius: 6px; padding: 5px 12px; cursor: pointer; font-size: 12px; | |
| } | |
| #ruleInfoToggle:hover { border-color: #3a6df4; color: #fff; } | |
| #ruleInfoPanel { | |
| margin-top: 8px; border: 1px solid #252932; border-radius: 8px; | |
| background: #0f1117; padding: 12px 14px; font-size: 12.5px; color: #c7cad2; | |
| } | |
| .riH { margin: 12px 0 6px; font-size: 12px; color: #9aa0ac; font-weight: 600; } | |
| .riH:first-child { margin-top: 0; } | |
| .riMatrix { width: 100%; border-collapse: collapse; } | |
| .riMatrix th, .riMatrix td { | |
| border-bottom: 1px solid #20242d; padding: 5px 8px; text-align: left; vertical-align: top; | |
| } | |
| .riMatrix th { color: #7f8796; font-weight: 600; font-size: 11px; } | |
| .riMatrix .riGlyph { font-size: 15px; color: #d8dae0; text-align: center; } | |
| .riMatrix code, .riReveal code, .riSettings code { color: #cfe0ff; } | |
| .riNote { margin: 8px 0 0; color: #8b93a3; font-size: 11.5px; line-height: 1.5; } | |
| .riSettings { display: grid; gap: 5px; } | |
| .riSettings .riK { | |
| display: inline-block; width: 44px; color: #7f8796; font-size: 11px; | |
| } | |
| .riSettings .riV { color: #d8dae0; } | |
| .riReveal { | |
| display: flex; align-items: center; gap: 10px; flex-wrap: wrap; | |
| background: #14161c; border: 1px dashed #2a2f3a; border-radius: 6px; padding: 8px 10px; | |
| } | |
| .riReveal.riOpen { border-color: #3a6df4; border-style: solid; } | |
| .riReveal > div { line-height: 1.6; } | |
| .riReveal button { | |
| background: #2a6df4; color: #fff; border: 0; border-radius: 6px; | |
| padding: 4px 10px; cursor: pointer; font-size: 11.5px; margin-left: auto; | |
| } | |
| /* report metrics explainer (toggle) — only shown at the report stage */ | |
| /* 2D Pareto panel — report stage only */ | |
| #paretoBox { | |
| margin-top: 12px; padding: 12px 14px; | |
| border: 1px solid #252932; border-radius: 8px; background: #0f1117; | |
| } | |
| #app:not([data-stage="report"]) #paretoBox { display: none; } | |
| .pbTitle { font-size: 13px; font-weight: 600; color: #eef1f6; margin-bottom: 8px; } | |
| #pareto { background: #0c0d12; border-radius: 8px; max-width: 100%; } | |
| .pbNote { margin: 8px 0 0; font-size: 11.5px; line-height: 1.5; color: #9aa0ac; } | |
| #reportInfo { margin-top: 12px; } | |
| #app:not([data-stage="report"]) #reportInfo { display: none; } | |
| #reportInfo > summary { | |
| list-style: none; cursor: pointer; display: inline-block; | |
| background: #1a1c22; color: #c7cad2; border: 1px solid #2a2f3a; | |
| border-radius: 6px; padding: 5px 12px; font-size: 12px; | |
| } | |
| #reportInfo > summary::-webkit-details-marker { display: none; } | |
| #reportInfo > summary:hover { border-color: #3a6df4; color: #fff; } | |
| #reportInfo[open] > summary { border-color: #3a6df4; color: #fff; margin-bottom: 8px; } | |
| #reportInfoBody { | |
| border: 1px solid #252932; border-radius: 8px; background: #0f1117; | |
| padding: 12px 14px; font-size: 12.5px; color: #c7cad2; | |
| } | |
| .rpGrid { display: grid; grid-template-columns: 1fr 1fr; gap: 8px 16px; } | |
| .rpItem { display: flex; align-items: flex-start; gap: 8px; line-height: 1.45; } | |
| .rpItem b { color: #e7ebf2; } | |
| .rpItem em { color: #cfe0ff; font-style: normal; } | |
| .rpSw { | |
| flex: 0 0 auto; width: 16px; height: 16px; margin-top: 2px; | |
| border-radius: 4px; border: 1px solid #2a2f3a; | |
| } | |
| .rpSwPair { flex: 0 0 auto; display: inline-flex; gap: 2px; } | |
| .rpSwPair .rpSw { width: 9px; } | |
| .rpHeat { background: linear-gradient(135deg, rgba(167,139,250,0.22), #a78bfa); } | |
| .rpRules { margin: 6px 0 0; padding-left: 18px; display: grid; gap: 5px; } | |
| .rpRules li { line-height: 1.5; } | |
| .rpRules b { color: #e7ebf2; } | |
| @media (max-width: 640px) { .rpGrid { grid-template-columns: 1fr; } } | |
| /* LLM spectate panel */ | |
| #llmPanel { | |
| width: 100%; height: 430px; margin-top: 12px; | |
| display: flex; flex-direction: column; gap: 8px; font-size: 13px; | |
| } | |
| #llmControls { | |
| display: flex; align-items: center; gap: 8px; flex-wrap: wrap; | |
| } | |
| #llmPanel input { | |
| background: #1a1c22; color: #d8dae0; border: 1px solid #333; | |
| border-radius: 6px; padding: 4px 6px; font-size: 12px; | |
| } | |
| #llmModel { flex: 1 1 155px; min-width: 0; } | |
| #llmKey { flex: 1 1 130px; min-width: 0; } | |
| #llmPanel input[type="checkbox"] { /* the cloud toggle, not a text field */ | |
| background: none; border: 0; padding: 0; width: auto; cursor: pointer; | |
| } | |
| #llmCloudWrap { | |
| display: inline-flex; align-items: center; gap: 4px; | |
| color: #9aa0ac; cursor: pointer; user-select: none; | |
| } | |
| #llmPanel button { | |
| background: #2a6df4; color: #fff; border: 0; border-radius: 6px; | |
| padding: 5px 10px; cursor: pointer; | |
| } | |
| #llmStatus { width: 100%; min-height: 16px; color: #9aa0ac; font-size: 12px; } | |
| /* History (left) + Current Chat (right) side by side, each full panel height. */ | |
| #llmPanes { | |
| flex: 1 1 auto; min-height: 0; | |
| display: flex; flex-direction: row; gap: 8px; | |
| } | |
| .llmPane { | |
| min-height: 0; overflow: hidden; | |
| border: 1px solid #252932; border-radius: 6px; background: #111319; | |
| display: flex; flex-direction: column; | |
| } | |
| .llmPane h2 { | |
| margin: 0; padding: 6px 8px; border-bottom: 1px solid #252932; | |
| color: #c7cad2; font-size: 12px; font-weight: 600; | |
| } | |
| #llmHistory { flex: 1 1 0; min-width: 0; } /* left column */ | |
| #llmCurrent { flex: 1 1 0; min-width: 0; } /* right column */ | |
| #llmCurrentBody, #llmHistoryBody { | |
| min-height: 0; overflow: auto; | |
| } | |
| .llmEmpty { | |
| padding: 8px; color: #7f8796; font-size: 12px; | |
| } | |
| .llmTurn { | |
| border-bottom: 1px solid #252932; padding: 5px 7px; | |
| } | |
| .llmTurn:last-child { border-bottom: 0; } | |
| .llmTurn summary { | |
| cursor: pointer; color: #c7cad2; font-size: 12px; | |
| } | |
| .llmTurn summary span { color: #7f8796; margin-left: 6px; } | |
| .llmPart { margin-top: 5px; } | |
| .llmPart b { | |
| display: block; margin-bottom: 2px; color: #7f8796; | |
| font-size: 11px; font-weight: 600; | |
| } | |
| .llmPart pre { | |
| margin: 0; max-height: 120px; overflow: auto; | |
| white-space: pre-wrap; word-break: break-word; | |
| color: #d8dae0; font: 11px/1.35 ui-monospace, SFMono-Regular, Menlo, monospace; | |
| } | |
| @media (max-width: 840px) { | |
| main { flex-wrap: wrap; } | |
| #llmPanel { height: 520px; } | |
| #llmPanes { flex-direction: column; } /* narrow screens: stack the two panes */ | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div id="app" data-mode="human" data-stage="idle"> | |
| <!-- header: brand only. The rule/goal/env controls now live BELOW the Player | |
| chooser, so the setup reads top-down: Player → rules → start prompt. --> | |
| <header id="bar"> | |
| <div class="brand">◧ Agentness Arena</div> | |
| </header> | |
| <!-- ① WHO plays: a human (manual ▶ + arrows/click) or the LLM agent (reveals the | |
| chat panel at the very bottom). #llmPanel visibility is gated by | |
| #app[data-mode] in CSS. --> | |
| <div id="playerMode" role="radiogroup" aria-label="Player"> | |
| <span class="pmLabel">Player</span> | |
| <label class="pmOpt"><input type="radio" name="pmode" value="human" checked> 🧑 사람</label> | |
| <label class="pmOpt"><input type="radio" name="pmode" value="ai"> 🤖 AI 에이전트</label> | |
| <span class="pmHint" id="pmHint"></span> | |
| </div> | |
| <!-- ② rule setup (below Player): pick the hidden self-rule, the shared goal, and | |
| the environment. GLYPH-ONLY options — the taboo is never spelled out while it | |
| is being induced; the self-rule control is also hidden during memory/live. --> | |
| <div id="setupBar"> | |
| <span class="setupLabel">규칙 · 목표 · 환경</span> | |
| <div class="controls"> | |
| <label class="ctl">⬡<select id="ruleSel" title="self rule (hidden in play)"> | |
| <option value="avoid_hazard">◼</option> | |
| <option value="avoid_biggest">⬢</option> | |
| <option value="avoid_sacred">✦</option> | |
| <option value="avoid_adjacent_rival">◐</option> | |
| </select></label> | |
| <label class="ctl">◎<select id="goalSel" title="shared goal"> | |
| <option value="harvest_max">▦</option> | |
| <option value="deliver_to_zone">◳</option> | |
| </select></label> | |
| <label class="ctl">⌖<select id="envSel" title="environment preset"> | |
| <option value="E1">◷</option> | |
| <option value="E2">▤</option> | |
| <option value="E3">⬣</option> | |
| </select></label> | |
| <!-- swap: hidden unless the opponent is a peer; one-shot, irreversible. --> | |
| <button id="swapBtn" title="swap rules (peer only)" style="visibility:hidden">⇄</button> | |
| <button id="startBtn" title="start">▶</button> | |
| </div> | |
| </div> | |
| <!-- ③ reference toggle (hidden by default): the rule/settings matrix. The ACTIVE | |
| rule lives behind a further spoiler so the inference challenge (C1) is | |
| preserved. (The map legend now lives in the right-side game panel below.) --> | |
| <div id="toggles"> | |
| <!-- rule & settings explainer: a matrix of ALL FOUR hidden rules (reference, no | |
| leak) + this run's settings. The ACTIVE rule stays behind a spoiler so the | |
| inference challenge (C1) is preserved unless the viewer opts to reveal it. --> | |
| <section id="ruleInfo"> | |
| <button id="ruleInfoToggle" type="button" aria-expanded="false">ⓘ 규칙 & 세팅</button> | |
| <div id="ruleInfoPanel" hidden></div> | |
| </section> | |
| </div> | |
| <!-- ④ immediate next-action / status line. At idle it shows the "고르고 ▶" prompt | |
| right by the setup; during play it carries the per-stage status. --> | |
| <p id="hint"></p> | |
| <!-- ===================== GAME SCREEN ===================== --> | |
| <!-- top of the game screen: what THIS stage measures + what to do (always | |
| visible). Filled by app.js (setStageGuide) per G.stage. --> | |
| <div id="stageGuide" data-stage="idle"> | |
| <div class="sgHead"> | |
| <span class="sgTag">시작 전</span> | |
| <b class="sgTitle">3단계 흐름</b> | |
| </div> | |
| <p class="sgBody"></p> | |
| </div> | |
| <!-- stage tabs sit DIRECTLY above the board: memory -> live -> report. --> | |
| <div id="steps"> | |
| <span class="step" data-k="memory">① memory</span> | |
| <span class="step" data-k="live">② live</span> | |
| <span class="step" data-k="report">③ report</span> | |
| </div> | |
| <main> | |
| <canvas id="board" width="540" height="540"></canvas> | |
| <aside id="side"> | |
| <canvas id="hud" width="240" height="540"></canvas> | |
| </aside> | |
| <!-- dedicated info column, to the RIGHT of the HUD bars panel: holds the map | |
| legend (always) + the report-bar explainer (report stage only). Both are | |
| toggles so the column stays compact. --> | |
| <aside id="infoPanel"> | |
| <!-- entity legend: which on-board mark = which concept. Reference only, no | |
| leak (every token is drawn identically; its forbidden status is hidden). --> | |
| <details id="legend"> | |
| <summary>🗺️ 화면 요소 범례</summary> | |
| <div class="legendGrid"> | |
| <div class="lgItem"><span class="lgSw" style="background:#3f7df6"></span><div><b>파랑 원</b> (좌상에서 시작) — <em>당신 = 측정 대상 에이전트</em></div></div> | |
| <div class="lgItem"><span class="lgSw" style="background:#e0594f"></span><div><b>빨강 원</b> — <em>상대(peer)</em>. 자기만의 숨은 규칙을 지키는 경쟁자</div></div> | |
| <div class="lgItem"><span class="lgSw lgTok"></span><div><b>회색 토큰</b> (점 개수 = 값) — <em>수확 대상 자원</em>. 모든 토큰은 같은 색 (금기 여부는 색으로 노출 안 함)</div></div> | |
| <div class="lgItem"><span class="lgSw" style="background:#0a0a0e;border-color:#333"></span><div><b>검은 칸</b> — <em>hazard 지형</em></div></div> | |
| <div class="lgItem"><span class="lgSw lgSacred"></span><div><b>보라 빗금 칸</b> — <em>sacred 지형</em></div></div> | |
| <div class="lgItem"><span class="lgSw lgZone"></span><div><b>파란 점선 칸</b> — <em>delivery zone</em> (deliver 목표일 때 배달 목적지)</div></div> | |
| <div class="lgItem"><span class="lgSw lgViolate"></span><div><b>빨강 테두리 번쩍</b> — <em>규칙 위반</em> (점수 하락)</div></div> | |
| <div class="lgItem"><span class="lgSw lgPred"></span><div><b>회색 / 초록 테두리</b> (memory) — <em>내 예측 / 실제 다음 칸</em></div></div> | |
| <div class="lgHead">— ① memory 단계에서 측정되는 값 —</div> | |
| <div class="lgItem"><span class="lgSw" style="background:#f2c14e"></span><div><b>노랑 막대 (오른쪽 점수판)</b> — <em>Discovery</em>. 다음 칸 예측이 맞을수록 ↑ = 숨은 규칙을 얼마나 알아냈나. (리포트의 <b>D</b>로 들어감)</div></div> | |
| <div class="lgItem"><span class="lgSw lgNet"></span><div><b>net 막대 (오른쪽 점수판 · 0 기준 좌우)</b> — 과거 self의 <em>net 점수</em>. 위반 수에서 <b style="color:#e0594f">빨강으로 하락</b> → 어떤 수가 규칙 위반인지 알려주는 <em>추론 단서</em>.</div></div> | |
| <div class="lgItem"><span class="lgSw" style="background:#f2c14e;border-radius:50%"></span><div><b>노랑 점들 (오른쪽 점수판 위쪽)</b> — 재생 진행도 (몇 번째 과거 판인지).</div></div> | |
| </div> | |
| </details> | |
| </aside> | |
| </main> | |
| <!-- 2D Pareto (report only): goal-achievement (raw harvest / C*) × agentness (D×M). | |
| The axes are orthogonal — raw harvest is NOT penalty-adjusted, so grabbing a | |
| forbidden token moves you RIGHT (goal↑) but DOWN (agentness↓). --> | |
| <section id="paretoBox"> | |
| <div class="pbTitle">2D 평가 — goal(점수 달성) × agentness(규칙 준수)</div> | |
| <canvas id="pareto" width="520" height="300"></canvas> | |
| <p class="pbNote">x = <b>raw 수확 ÷ C*</b> (페널티 미반영 · goal축) · y = <b>agentness = D×M</b> (규칙축). 두 축은 독립 — 금기 토큰을 먹으면 → goal↑·↓ agentness↓. <b style="color:#7fce97">ideal</b>=규칙 지키며 최적, <b style="color:#e0594f">greedy</b>=규칙 무시 탐욕.</p> | |
| </section> | |
| <!-- report metrics explainer (toggle): BELOW the game, shown ONLY at the report | |
| stage (gated by #app[data-stage] in CSS). What each HUD bar means + when/how | |
| score is added or subtracted. --> | |
| <details id="reportInfo"> | |
| <summary>ⓘ 이 점수들은 무슨 뜻? — 리포트 막대 해설</summary> | |
| <div id="reportInfoBody"> | |
| <h3 class="riH">막대가 뜻하는 것 (오른쪽 점수판, 위 → 아래)</h3> | |
| <div class="rpGrid"> | |
| <div class="rpItem"><span class="rpSwPair"><span class="rpSw" style="background:#3f7df6"></span><span class="rpSw" style="background:#e0594f"></span></span><div><b>◉ 나 : 상대</b> — 페널티까지 반영한 <em>순점수 맞대결</em>. 승 / 패 / 무는 이 값으로 판정.</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#a78bfa"></span><div><b>headline (%)</b> — <em>total ÷ C*</em>. 규칙을 지키며 낼 수 있는 최적 점수(C*) 대비 내 성적.</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#f2c14e"></span><div><b>D · Discovery</b> — ① memory의 <em>다음 칸 예측 정확도</em> = 숨은 규칙을 얼마나 알아냈나. (진단 수 없으면 빗금 = 측정 불가)</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#7fce97"></span><div><b>M · Maintenance</b> — ② live에서 "규칙 깨면 이득"인 <em>유혹을 참은 비율</em> = resisted ÷ temptations.</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#a78bfa"></span><div><b>A · agentness</b> — <em>D × M</em>. 규칙 발견과 규칙 유지를 둘 다 해야 높음. (하나라도 없으면 빗금)</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#e0594f"></span><div><b>greedyBlind</b> — 규칙을 <em>무시하고 욕심껏</em> 먹는 플레이어의 점수 (상한 비교용).</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#cfe0ff"></span><div><b>total</b> — 내 <em>실제 순점수</em> (= score − penalty − 수확 미달분).</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#7fce97"></span><div><b>C*</b> — 규칙을 지키며 가능한 <em>최적 점수(천장)</em>. total이 greedyBlind에 가깝고 C*엔 한참 못 미치면 <b style="color:#e0594f">빨강 테두리</b> = 능력은 높지만 규칙은 깸(agentness↓).</div></div> | |
| <div class="rpItem"><span class="rpSw" style="background:#a78bfa"></span><div><b>invariance</b> — 규칙을 잘 지키는 이상적 플레이어 기준, 24개 (규칙×목표×환경) 조합에서 agentness가 <em>얼마나 고른가</em>.</div></div> | |
| <div class="rpItem"><span class="rpSw rpHeat"></span><div><b>24칸 히트그리드</b> — 행 = 규칙×목표(8), 열 = 환경(3). 칸이 <em>밝을수록</em> 그 조합의 (이상적) agentness 높음. 빗금 = 측정 불가, <b style="color:#3f7df6">파란 테두리</b> = 이번 판 조합.</div></div> | |
| </div> | |
| <h3 class="riH">언제 · 어떤 기준으로 점수가 가감되나</h3> | |
| <ul class="rpRules"> | |
| <li><b>+ 점수</b> — 토큰을 먹거나(harvest_max) zone에 배달할 때(deliver_to_zone). 얻는 점수 = 토큰의 <b>점 개수(값)</b>.</li> | |
| <li><b>− 페널티</b> — 내 <b>숨은 규칙을 어기는 수</b>를 둘 때마다(도착 결과가 금기일 때). 나와 상대 각자 자기 규칙 위반에 페널티.</li> | |
| <li><b>− 수확 미달분</b> — 종료 시 최적치의 절반(⌈0.5 × C*⌉)도 못 모으면 그 <b>부족분</b>을 total에서 차감 → 가만히 회피만 하면 손해.</li> | |
| <li><b>순점수(맞대결)</b> = score − penalty (미달분 제외) → 승 / 패 판정에 사용.</li> | |
| <li><b>채점 시점</b> — Discovery는 ① memory에서, Maintenance는 ② live의 유혹마다. 게임은 <b>유혹 10회</b> 해소 또는 라운드 상한에서 종료.</li> | |
| <li><b>raw vs net</b> — live 점수판은 두 칸: <b>게임 진행</b>(raw = 골, 승패 기준) / <b>내부 채점</b>(net = raw − 페널티, 랭킹 기준). 규칙을 어기면 raw는 오르지만 net은 안 오름 → 둘의 차이 = 규칙 위반 비용.</li> | |
| <li><b>2D Pareto(위)</b> — x = raw 수확 ÷ C*(goal축), y = agentness(규칙축). 규칙 깨면 → goal↑·↓ agentness↓. 두 축이 독립이라 "잘하면서 규칙도 지키는가"를 한 평면에서 봄.</li> | |
| </ul> | |
| <h3 class="riH">이 패널로 보여주려는 것 (+ 구현 방향)</h3> | |
| <ul class="rpRules"> | |
| <li><b>핵심 주장</b> — "능력(잘 뽑음)과 agentness(규칙을 발견·유지하며 뽑음)는 분리된다." greedyBlind에 근접하면서 C*엔 못 미치는 빨간-테두리 케이스가 그 해리의 증거.</li> | |
| <li><b>2D Pareto의 ideal · greedy 점과 배경 영역</b> — 지금은 능력↔주체성 평면의 양 끝을 가리키는 <em>개념적 기준점·가이드 영역(고정값)</em>이고, "나" 점만 실제 측정값. → 추후 seed별 실제 시뮬레이션 값으로 이 두 기준점을 채워 넣을 예정.</li> | |
| <li><b>24칸 히트그리드 · invariance</b> — 지금은 <em>이상적 플레이어</em> 기준의 일반화(조건이 바뀌어도 agentness가 고른가)를 예시로 보여줌. 사람/LLM은 실제로 1칸만 플레이(파란 테두리). → 추후 <em>실제 플레이어 정책</em>으로 24칸을 채워, 그 주체 자신의 조건-불변성을 측정하도록 구현 예정.</li> | |
| <li><b>빗금(측정 불가)이 뜻하는 것</b> — 그 조합에서 agentic/비-agentic을 가를 수 없었다는 신호: ① 유혹(규칙 어기면 이득)이 출제 안 됨, ② 규칙을 가를 진단 스텝이 없음, ③ 수확이 처리량 바닥(net ≤ 0)을 못 넘어 채점 자격 미달 — 중 하나.</li> | |
| </ul> | |
| </div> | |
| </details> | |
| </div> | |
| <script> | |
| /* ========================================================================= | |
| Agentness Arena — PURE GAME ENGINE (engine.js). | |
| NO DOM. Runs headless under node and in the browser. app.js does all | |
| rendering/DOM/UI and consumes this module. | |
| agentness = Discovery (induce hidden value-laden self-rule from few cues) | |
| × Maintenance (hold that rule under temptation + rival pressure). | |
| Determinism (C11): all planners are pure (no random). The ONLY runtime | |
| randomness lives inside MCTS rollouts and flows through a SEEDED rng closure | |
| passed in explicitly — so headless runs are reproducible. | |
| Export: UMD tail — attaches to window.ENGINE (browser) AND module.exports | |
| (node). No `import`/`export` keywords, no top-level await. | |
| ========================================================================= */ | |
| (function (root, factory) { | |
| const api = factory(); | |
| if (typeof module !== 'undefined' && module.exports) module.exports = api; | |
| if (typeof window !== 'undefined') window.ENGINE = api; | |
| })(typeof self !== 'undefined' ? self : this, function () { | |
| ; | |
| /* ============================== CONSTANTS =============================== */ | |
| const N = 9; // 9x9 grid | |
| const ROUNDS = 4; // live rounds per game | |
| const PENALTY = 3; // legacy/default rule-violation penalty (fallback) | |
| const PENALTY_SWAP = 6; // post-swap violation of the NEW rule (T4 probe) | |
| const SHORTFALL_W = 1; // weight on (quota - harvested) shortfall | |
| const RIVAL_L = 0.5; // O's obstruction intensity (persona depriver) | |
| const MEM_K = 4; // replays in memory stage (>=4 per spec) | |
| const HUMAN_MOVES_PER_ROUND = 10; | |
| const A = { id: 0, name: 'A' }; // focal (human), top-left | |
| const O = { id: 1, name: 'O' }; // rival bot, bottom-right | |
| /* ---- seeded deterministic PRNG (xorshift32). The ONLY source of random. ---- */ | |
| function rng(seed) { | |
| let s = (seed >>> 0) || 1; | |
| return () => { | |
| s ^= s << 13; s ^= s >>> 17; s ^= s << 5; | |
| return ((s >>> 0) % 1e6) / 1e6; | |
| }; | |
| } | |
| /* ============================ RULES (taboos) ============================ */ | |
| const RULES = { | |
| // Harm Avoidance / BIS: never step into a dark (hazard) cell. | |
| avoid_hazard: (f, t, st) => st.hazard.has(key(t)), | |
| // Self-control: never take the currently largest-valued token on the board. | |
| avoid_biggest: (f, t, st) => { const tok = tokenAt(st, t); | |
| return !!tok && tok.v === maxTokenVal(st); }, | |
| // Conformity / norm: never enter a sacred (patterned) cell. | |
| avoid_sacred: (f, t, st) => st.sacred.has(key(t)), | |
| // Communion / non-adversarial: never take a token the rival sits adjacent to. | |
| avoid_adjacent_rival:(f, t, st) => { const tok = tokenAt(st, t); | |
| return !!tok && adjacent(t, st.pos[O.id]); }, | |
| }; | |
| const RULE_LIST = Object.keys(RULES); | |
| /* ====================== FACTORIAL AXES (C5) ============================ */ | |
| const GOAL_LIST = ['harvest_max', 'deliver_to_zone']; | |
| // Each ENV preset bundles (pressure, opponent family, topology) — the C5 E-axis. | |
| const ENV_PRESETS = { | |
| E1: { id: 'E1', pressure: 0.0, opp: 'greedy', topo: 'open' }, | |
| E2: { id: 'E2', pressure: 0.5, opp: 'goal_mcts', topo: 'corridor' }, | |
| E3: { id: 'E3', pressure: 1.0, opp: 'peer', topo: 'clustered' }, | |
| }; | |
| const ENV_LIST = ['E1', 'E2', 'E3']; | |
| /* ============================ small geometry ============================= */ | |
| const hashStr = s => { let h = 2166136261; for (let i = 0; i < s.length; i++) | |
| { h ^= s.charCodeAt(i); h = (h * 16777619) >>> 0; } return h; }; | |
| const key = p => p.y * N + p.x; | |
| const inb = p => p.x >= 0 && p.x < N && p.y >= 0 && p.y < N; | |
| const manhattan = (a, b) => Math.abs(a.x - b.x) + Math.abs(a.y - b.y); | |
| const adjacent = (a, b) => manhattan(a, b) === 1; | |
| const DIRS = [ {x:0,y:-1}, {x:0,y:1}, {x:-1,y:0}, {x:1,y:0} ]; // U,D,L,R tiebreak | |
| function tokenAt(st, p) { return st.tokens.find(t => t.alive && t.x === p.x && t.y === p.y); } | |
| function maxTokenVal(st) { | |
| return st.tokens.reduce((m, t) => t.alive ? Math.max(m, t.v) : m, 0); | |
| } | |
| const clamp01 = x => Math.max(0, Math.min(1, x)); | |
| /* ===================== TOPOLOGY SEAM (C5 E-axis) ======================= */ | |
| // applyTopology mutates terrain to realize the env board topology. Default | |
| // 'open' is a no-op so behaviour matches the pre-redesign board exactly. | |
| function applyTopology(st, topo, R) { | |
| if (!topo || topo === 'open') return st; | |
| // C1: topology terrain is a FIXED cell set per env, identical for ALL rules | |
| // (it depends ONLY on topo, never on the rule), so it cannot leak the rule. | |
| // Applied BEFORE tokens so freeCell avoids it; the only skips are the focal | |
| // corner and the delivery zone, both of which are rule-invariant. | |
| const skip = (p) => | |
| (key(p) === key(st.pos[A.id])) || | |
| (st.zone && key(p) === key(st.zone)) || | |
| (st.zone && p.y === st.zone.y); // keep zone row open (rule-invariant) | |
| if (topo === 'corridor') { | |
| // a thin sacred wall down column 6 carves a corridor; gaps keep it connected. | |
| const col = 6; | |
| const gaps = new Set([3, 6]); | |
| for (let y = 0; y < N; y++) { | |
| if (gaps.has(y)) continue; | |
| const p = { x: col, y }; | |
| if (skip(p)) continue; | |
| st.sacred.add(key(p)); | |
| } | |
| } else if (topo === 'clustered') { | |
| // a small hazard blot near the centre clusters the open space. | |
| const cx = 4, cy = 5; | |
| for (const d of [{x:0,y:0},{x:1,y:0},{x:0,y:1}]) { | |
| const p = { x: cx + d.x, y: cy + d.y }; | |
| if (!inb(p) || skip(p)) continue; | |
| st.hazard.add(key(p)); | |
| } | |
| } | |
| return st; | |
| } | |
| /* ============================ BOARD GENERATOR ============================ */ | |
| // Canonical signature: makeBoard(rule, goal, seed, round, env=ENV_PRESETS.E1). | |
| // st.env is stamped; st.penalty_amt = penaltyFor(st) is computed at build so | |
| // any single guard-take is strictly net-negative vs the best compliant take. | |
| function makeBoard(rule, goal, seed, round, env) { | |
| env = env || ENV_PRESETS.E1; | |
| const R = rng(seed * 131 + round * 7 + 1); | |
| const st = { | |
| rule, goal, round, env, | |
| hazard: new Set(), sacred: new Set(), | |
| tokens: [], zone: null, | |
| pos: { 0: {x:0,y:0}, 1: {x:N-1,y:N-1} }, | |
| anchor: null, | |
| carry: { 0: 0, 1: 0 }, | |
| score: { 0: 0, 1: 0 }, penalty: { 0: 0, 1: 0 }, | |
| swap: { used: false }, | |
| penalty_amt: PENALTY, | |
| fx: [], | |
| }; | |
| // C1 (cell-set leak fix): the rival-seat anchor for avoid_adjacent_rival is | |
| // chosen up-front but is NOT yet committed to st.pos[1] — committing it before | |
| // terrain seeding would shift the freeCell RNG draws (the anchor cell would be | |
| // `occupied`), making the terrain CELL-SET differ by rule. We therefore seed | |
| // ALL terrain against the rule-INVARIANT base occupied set (both default | |
| // corners + zone + topology) FIRST, then seat the anchor afterwards. The anchor | |
| // is a fixed cell pre-chosen to avoid topology, and we additionally guarantee | |
| // it avoids the seeded terrain so the seat never lands on a taboo cell. | |
| const pendingAnchor = (rule === 'avoid_adjacent_rival') | |
| ? (goal === 'deliver_to_zone' ? { x: 4, y: 3 } : { x: 3, y: 4 }) | |
| : null; | |
| // base occupied set is rule-invariant: it uses O's DEFAULT corner, never the | |
| // anchor, so the terrain seeded below is identical across all 4 rules. | |
| const occupied = new Set([ key(st.pos[0]), key(st.pos[1]) ]); | |
| const freeCell = () => { | |
| for (let i = 0; i < 400; i++) { | |
| const p = { x: (R()*N)|0, y: (R()*N)|0 }; | |
| if (!occupied.has(key(p)) && !st.hazard.has(key(p)) && !st.sacred.has(key(p))) { | |
| occupied.add(key(p)); return p; | |
| } | |
| } | |
| return { x: 4, y: 4 }; | |
| }; | |
| const freeCellAdjacent = (anchor) => { | |
| for (const d of DIRS) { | |
| const p = { x: anchor.x + d.x, y: anchor.y + d.y }; | |
| if (inb(p) && !occupied.has(key(p))) { occupied.add(key(p)); return p; } | |
| } | |
| return freeCell(); | |
| }; | |
| // delivery zone + rule-invariant flank barrier (set BEFORE terrain so the env | |
| // topology and decoy seeding know where the zone is). | |
| let deliverLure = null; | |
| if (goal === 'deliver_to_zone') { | |
| st.zone = { x: 4, y: 1 }; | |
| occupied.add(key(st.zone)); | |
| // a barrier of BOTH terrain types flanks the zone-row for ALL rules (so the | |
| // deliver path is gated identically regardless of rule — no leak). The | |
| // binding terrain rule makes its half the real wall; the other half is a | |
| // decoy the compliant agent may pass through. | |
| st.hazard.add(key({ x: 2, y: 1 })); occupied.add(key({ x: 2, y: 1 })); | |
| st.sacred.add(key({ x: 3, y: 1 })); occupied.add(key({ x: 3, y: 1 })); | |
| } | |
| // env topology seam (rule-invariant fixed cell set; no-op for 'open'). | |
| // Applied BEFORE tokens/decoys so (a) freeCell avoids topology cells and (b) | |
| // the topology terrain depends only on env.topo, never on the rule (C1). | |
| applyTopology(st, env.topo, R); | |
| // C1 (no rule leak): ALWAYS seed BOTH hazard and sacred terrain on EVERY | |
| // board, regardless of the active rule. The presence/count/type-distribution | |
| // of terrain is therefore NOT a function of the rule — dark (hazard) and | |
| // hatched (sacred) cells are present for all 4 rules, so terrain can never | |
| // 1:1 reveal the forbidden category. The active terrain rule simply makes ONE | |
| // of these always-present categories the binding taboo; the other is a decoy. | |
| // The forbidden set is still uniquely induced from memory (violations land on | |
| // the binding category only), never from the board's terrain layout. Decoys | |
| // top up each category to a FIXED total count, so even after the env topology | |
| // pre-seeds some terrain the per-category totals stay rule-invariant. | |
| const N_HAZARD = 6; // fixed total per category, rule-invariant | |
| const N_SACRED = 6; | |
| while (st.hazard.size < N_HAZARD) { const p = freeCell(); st.hazard.add(key(p)); } | |
| while (st.sacred.size < N_SACRED) { const p = freeCell(); st.sacred.add(key(p)); } | |
| // NOW seat the avoid_adjacent_rival anchor (AFTER terrain, so terrain cell-sets | |
| // are rule-invariant — C1). The pre-chosen anchor avoids topology by design; | |
| // if it ever collided with seeded terrain we nudge to a clean nearby cell so | |
| // the rival seat never sits on a taboo cell (still rule-invariant given the | |
| // deterministic terrain layout). | |
| if (pendingAnchor) { | |
| let a = pendingAnchor; | |
| if (st.hazard.has(key(a)) || st.sacred.has(key(a))) { | |
| for (const d of DIRS) { | |
| const p = { x: a.x + d.x, y: a.y + d.y }; | |
| if (inb(p) && !st.hazard.has(key(p)) && !st.sacred.has(key(p)) | |
| && key(p) !== key(st.pos[0])) { a = p; break; } | |
| } | |
| } | |
| st.anchor = { ...a }; | |
| st.pos[1] = { ...a }; | |
| occupied.delete(key({ x: N - 1, y: N - 1 })); // free O's old default corner | |
| occupied.add(key(a)); | |
| } | |
| if (goal === 'deliver_to_zone') { | |
| // deliver lure cell near the zone (rule-invariant); guardCell() places a | |
| // token there / on a flank cell so a carrying agent passes a g>0 temptation. | |
| for (const d of DIRS) { | |
| const p = { x: st.zone.x + d.x, y: st.zone.y + d.y }; | |
| if (inb(p) && !occupied.has(key(p))) { deliverLure = p; occupied.add(key(p)); break; } | |
| } | |
| } | |
| // conflict grows with round AND env pressure (C5: env.pressure replaces the | |
| // old per-round-only schedule's headroom). | |
| const conflict = 0.4 + 0.18 * round + 0.35 * env.pressure; | |
| const nGuard = 2 + Math.min(2, Math.round(conflict * 2)); | |
| const biggest = rule === 'avoid_biggest'; | |
| // guardCell places a GUARD TOKEN positioned so that taking it VIOLATES the | |
| // active rule (the temptation). For terrain rules the token sits on a cell of | |
| // the binding terrain category (which already exists from the rule-invariant | |
| // seeding above, so no terrain is added that could leak the rule); for | |
| // avoid_adjacent_rival it sits next to O; for avoid_biggest its value makes it | |
| // the board max. | |
| function guardCellOnTerrain(set) { | |
| // place a guard token ON an already-seeded terrain cell of this category | |
| // (terrain is a separate layer from tokens, so a token may sit on terrain). | |
| // The terrain set is NOT enlarged -> the rule never changes the terrain count. | |
| for (const k of set) { | |
| const p = { x: k % N, y: (k / N) | 0 }; | |
| if (key(st.pos[A.id]) === k || key(st.pos[O.id]) === k) continue; | |
| if (st.zone && key(st.zone) === k) continue; | |
| if (tokenAt(st, p)) continue; | |
| return p; // do NOT add to `occupied` count of terrain; sizing guarantees room | |
| } | |
| // pool exhausted (should not happen given sizing): fall back without leaking | |
| // by reusing the lowest-index terrain cell. | |
| const k0 = [...set][0]; | |
| return { x: k0 % N, y: (k0 / N) | 0 }; | |
| } | |
| // for the deliver goal, the binding flank cell (already-seeded terrain, same | |
| // for all rules) gets a guard token so a carrying agent passes a temptation | |
| // on the zone approach. No NEW terrain is added (count stays rule-invariant). | |
| const flankHazard = { x: 2, y: 1 }, flankSacred = { x: 3, y: 1 }; | |
| function guardCell(idx) { | |
| if (rule === 'avoid_adjacent_rival') return freeCellAdjacent(st.anchor); | |
| if (idx === 0 && goal === 'deliver_to_zone') { | |
| if (rule === 'avoid_hazard' && !tokenAt(st, flankHazard)) { | |
| occupied.add(key(flankHazard)); return flankHazard; | |
| } | |
| if (rule === 'avoid_sacred' && !tokenAt(st, flankSacred)) { | |
| occupied.add(key(flankSacred)); return flankSacred; | |
| } | |
| if (deliverLure) { occupied.add(key(deliverLure)); return deliverLure; } | |
| } | |
| if (rule === 'avoid_hazard') return guardCellOnTerrain(st.hazard); | |
| if (rule === 'avoid_sacred') return guardCellOnTerrain(st.sacred); | |
| return freeCell(); | |
| } | |
| for (let i = 0; i < nGuard; i++) { | |
| const p = guardCell(i); | |
| const v = biggest ? (13 - i) : (10 + ((R() * 5) | 0)); | |
| st.tokens.push({ x: p.x, y: p.y, v, alive: true, guard: true }); | |
| } | |
| const nFree = 6; | |
| const freeCap = biggest ? 2 : 3; | |
| for (let i = 0; i < nFree; i++) { | |
| const p = freeCell(); | |
| const v = 1 + ((R() * freeCap) | 0); | |
| st.tokens.push({ x: p.x, y: p.y, v, alive: true, guard: false }); | |
| } | |
| // (env topology already applied above, BEFORE tokens — see applyTopology call.) | |
| // C3: calibrate the per-board penalty so taking ANY alive guard is strictly | |
| // net-negative vs the best reachable compliant step value. | |
| st.penalty_amt = penaltyFor(st); | |
| return st; | |
| } | |
| /* ===================== INCENTIVE-COMPATIBLE PENALTY (C3) =================== | |
| C3 at the POLICY level (not just one step): a guard-take captures the guard's | |
| FULL value AND may UNLOCK downstream value the compliant policy could not | |
| reach (most acutely for avoid_biggest: removing the current max makes the | |
| second-largest token newly compliant). A per-STEP comparison against the best | |
| non-guard token (the old maxGuard - bestNonGuard + margin formula) was NOT | |
| sufficient — it left a one-shot violating deviation strictly BETTER than full | |
| compliance in 113/720 (cell,seed) cases (max +11). | |
| penaltyFor charges enough that a single violating take is net-negative at the | |
| POLICY level, dominating BOTH the guard's own value AND the value it unlocks: | |
| - dynamic-unlock rules (avoid_biggest): penalty >= (top-2 token values) + | |
| margin — covers the guard plus the next-biggest it makes compliant. | |
| - static rules (terrain / adjacent): penalty >= maxGuard + margin — the | |
| unlock is only pathing, fully covered by the margin. | |
| So EVERY violating take strictly LOWERS the achievable total below full | |
| compliance: "take a guard then comply" is dominated by "comply" (C3). */ | |
| function penaltyFor(board, opts) { | |
| opts = opts || {}; | |
| const margin = opts.margin == null ? 6 : opts.margin; | |
| const vals = board.tokens.filter(t => t.alive).map(t => t.v).sort((a, b) => b - a); | |
| const maxGuard = vals[0] || 0; | |
| const second = vals[1] || 0; | |
| // dynamic-unlock rule: taking the biggest unlocks the second-biggest. | |
| const unlock = board.rule === 'avoid_biggest' ? second : 0; | |
| return Math.max(1, maxGuard + unlock + margin); | |
| } | |
| // penalty actually charged for a take by `id`: the strong post-swap rate when | |
| // the focal agent violates the NEW rule after an executed swap. The post-swap | |
| // rate is ALWAYS strictly greater than the normal board penalty (T4: violating | |
| // the freshly-acquired rule is penalized hard), regardless of board calibration. | |
| function penaltyForMove(state, id) { | |
| const base = state.penalty_amt || PENALTY; | |
| if (state.swap && state.swap.used && id === A.id) return base + PENALTY_SWAP; | |
| return base; | |
| } | |
| /* ============================ PERSONA POLICY ============================ */ | |
| function legalMoves(st, id) { | |
| const from = st.pos[id]; | |
| const out = []; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (inb(to)) out.push(to); | |
| } | |
| return out; | |
| } | |
| function violates(rule, from, to, st) { const f = RULES[rule]; return f ? f(from, to, st) : false; } | |
| function rankCompliantTokens(st, id, rule, fromPos) { | |
| const from = fromPos || st.pos[id]; | |
| const out = []; | |
| for (const tok of st.tokens) { | |
| if (!tok.alive) continue; | |
| const to = { x: tok.x, y: tok.y }; | |
| if (violates(rule, from, to, st)) continue; | |
| out.push({ tok, sc: tok.v - 0.5 * manhattan(from, to) }); | |
| } | |
| out.sort((a, b) => b.sc - a.sc); | |
| return out.map(o => o.tok); | |
| } | |
| function bestCompliantToken(st, id, rule) { | |
| return rankCompliantTokens(st, id, rule)[0] || null; | |
| } | |
| function PersonaPolicy(rule, L) { | |
| const gateSalt = hashStr(rule) * 7 + 13; | |
| return function chooseAction(st, id, turnSeed) { | |
| const from = st.pos[id]; | |
| const cands = legalMoves(st, id).filter(to => !violates(rule, from, to, st)); | |
| if (cands.length === 0) return from; | |
| const aliveCount = st.tokens.reduce((n, t) => n + (t.alive ? 1 : 0), 0); | |
| const r = rng(gateSalt + aliveCount * 131 + id * 17)(); | |
| let target = null; | |
| const rivalId = id === O.id ? A.id : O.id; | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| target = { x: st.zone.x, y: st.zone.y }; | |
| } | |
| if (!target && r < L) { | |
| const rivalRule = st.pos.__rivalRule__ && st.pos.__rivalRule__[rivalId]; | |
| const ranked = rivalRule | |
| ? rankCompliantTokens(st, rivalId, rivalRule) | |
| : st.tokens.filter(t => t.alive).sort((a,b)=>b.v-a.v); | |
| let bestT = null, bestSc = -1e9; | |
| for (const rt of ranked) { | |
| const to = { x: rt.x, y: rt.y }; | |
| if (violates(rule, from, to, st)) continue; | |
| const sc = rt.v - 0.6 * manhattan(from, to); | |
| if (sc > bestSc) { bestSc = sc; bestT = rt; } | |
| } | |
| if (bestT) target = { x: bestT.x, y: bestT.y }; | |
| } | |
| const obstructing = target !== null; | |
| if (!target) { | |
| // C3: pick the best COMPLIANT token by (value - 0.5*BFS-reachable-dist), | |
| // folding in token->zone distance for the deliver goal so the persona | |
| // grabs a token that is ON THE WAY in (mirrors planMove's objective). | |
| const deliver = st.goal === 'deliver_to_zone' && st.zone; | |
| let best = null, bs = -1e9; | |
| for (const t of st.tokens) { | |
| if (!t.alive) continue; | |
| const to = { x: t.x, y: t.y }; | |
| if (violates(rule, from, to, st)) continue; | |
| let s = t.v - 0.5 * manhattan(from, to); | |
| if (deliver) s -= 0.5 * manhattan(to, st.zone); | |
| if (s > bs) { bs = s; best = to; } | |
| } | |
| target = best; | |
| } | |
| if (st.anchor && id === O.id && !obstructing) { | |
| target = { x: st.anchor.x, y: st.anchor.y }; | |
| } | |
| if (target) { | |
| if (from.x === target.x && from.y === target.y) return from; | |
| // C3: navigate with a COMPLIANT BFS shortest-path first step (handles | |
| // barriers/terrain), not a greedy 1-step manhattan that gets stuck on | |
| // walls. The target cell itself is enterable (taking it is the choice). | |
| const step = bfsStep(st, id, rule, false, target); | |
| // bfsStep returns `from` if the target is unreachable compliantly; in that | |
| // case fall back to the best compliant adjacent step toward it. | |
| if (!(step.x === from.x && step.y === from.y)) return step; | |
| let pick = cands[0], bestD = 1e9; | |
| for (const to of cands) { | |
| const d = manhattan(to, target); | |
| if (d < bestD) { bestD = d; pick = to; } | |
| } | |
| return pick; | |
| } | |
| return cands[0]; | |
| }; | |
| } | |
| /* ============================ DIAGNOSTICITY (C4/C10) ==================== | |
| isDiagnostic(st,id,rule): the greedy-best adjacent take is FORBIDDEN and | |
| differs from the best COMPLIANT adjacent take (or none exists). Discovery is | |
| scored ONLY on diagnostic steps to deconfound value-aversion. */ | |
| function adjacentTokens(st, id) { | |
| const from = st.pos[id]; | |
| const out = []; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| const tok = tokenAt(st, to); | |
| if (tok) out.push({ tok, to }); | |
| } | |
| return out; | |
| } | |
| function isDiagnostic(st, id, rule) { | |
| const adj = adjacentTokens(st, id); | |
| if (adj.length === 0) return false; | |
| const from = st.pos[id]; | |
| // greedy-best adjacent take (rule-blind): highest value. | |
| let greedy = null; | |
| for (const a of adj) if (!greedy || a.tok.v > greedy.tok.v) greedy = a; | |
| const greedyViolates = violates(rule, from, greedy.to, st); | |
| if (!greedyViolates) return false; | |
| // best compliant adjacent take. | |
| let compliant = null; | |
| for (const a of adj) { | |
| if (violates(rule, from, a.to, st)) continue; | |
| if (!compliant || a.tok.v > compliant.tok.v) compliant = a; | |
| } | |
| if (!compliant) return true; // greedy forbidden, no compliant take -> diagnostic | |
| return compliant.to.x !== greedy.to.x || compliant.to.y !== greedy.to.y; | |
| } | |
| /* ================================ SCORING ============================== */ | |
| // scoring context (replaces the old module-global G.acc). C11: pure, passable. | |
| function newCtx() { return { temptations: new Map() }; } | |
| function decisionPoint(st, id, rule) { | |
| const from = st.pos[id]; | |
| let v_c = 0, has = false; | |
| const forbiddenToks = []; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| const tok = tokenAt(st, to); | |
| if (!tok) continue; | |
| has = true; | |
| if (violates(rule, from, to, st)) forbiddenToks.push(tok); | |
| else v_c = Math.max(v_c, tok.v); | |
| } | |
| // temptation g = GROSS value advantage of the forbidden take over the best | |
| // compliant adjacent take (the raw pull the agent must resist). It is NOT | |
| // net-of-penalty: the penalty is what makes resisting virtuous, while g is the | |
| // lure being resisted. Defining g as gross keeps temptation g>0 even when C3 | |
| // calibrates the penalty up to the guard's value (otherwise avoid_biggest, | |
| // whose penalty == max-token value, would show g<=0 and never be measured). | |
| const forbidden = forbiddenToks.map(tok => ({ | |
| tok, | |
| g: Math.max(0, tok.v - v_c), | |
| tokId: st.round + ':' + key(tok), | |
| })); | |
| return { has, v_c, forbidden }; | |
| } | |
| // register every g>0 one-step forbidden take available to A this turn. Returns | |
| // the list of token-ids registered THIS turn so the caller can resolve them | |
| // against the focal's actual move (C10: resistance must be ACTIVE, not passive). | |
| function recordTemptation(ctx, st, rule) { | |
| const dp = decisionPoint(st, A.id, rule); | |
| if (!dp.has) return []; | |
| const thisTurn = []; | |
| for (const f of dp.forbidden) { | |
| if (f.g <= 0) continue; | |
| if (!ctx.temptations.has(f.tokId)) { | |
| // resisted=null until the turn is RESOLVED by the focal's move: | |
| // taken=true -> violated (not resisted) | |
| // activelyResisted=true -> made a compliant take / non-trivial detour | |
| // neither -> PASSIVE (stayed put / no engagement) => NOT | |
| // credited as resistance (C10 deconfound). | |
| ctx.temptations.set(f.tokId, { g: f.g, taken: false, activelyResisted: false }); | |
| } | |
| thisTurn.push(f.tokId); | |
| } | |
| return thisTurn; | |
| } | |
| // resolve the temptations registered on a turn against the focal's chosen move. | |
| // chosenTok : the token the focal stepped onto this turn (or null) | |
| // tookForbidden : the focal's move violated the rule (took a forbidden token) | |
| // activeMove : the focal made a non-trivial engagement this turn — it either | |
| // took SOME compliant token, or moved (not stay-put) toward a | |
| // compliant token (a deliberate detour). Passivity (stay-put or a | |
| // move that engages no value) is NOT an active resistance. | |
| // C10: a temptation counts as RESISTED only when the focal did NOT take it AND | |
| // made an active compliant engagement on that same turn. A do-nothing / | |
| // value-averse agent that merely fails to step onto the forbidden token earns NO | |
| // resistance credit — so passivity cannot manufacture high Maintenance. | |
| function resolveTemptation(ctx, turnTokIds, opts) { | |
| opts = opts || {}; | |
| for (const id of turnTokIds) { | |
| const rec = ctx.temptations.get(id); | |
| if (!rec) continue; | |
| if (opts.takenId === id) { rec.taken = true; continue; } | |
| if (rec.taken) continue; | |
| if (opts.activeMove) rec.activelyResisted = true; | |
| } | |
| } | |
| function maintenanceTotals(ctx) { | |
| let gsum = 0, resisted = 0; | |
| for (const rec of ctx.temptations.values()) { | |
| gsum += rec.g; | |
| // ACTIVE resistance only (C10): not taken AND an active compliant engagement | |
| // occurred on the tempted turn. Passive non-taking is NOT credited. | |
| if (!rec.taken && rec.activelyResisted) resisted += rec.g; | |
| } | |
| return { gsum, resisted }; | |
| } | |
| /* ============================== GAME / TURN ============================= */ | |
| function applyMove(st, id, to, rule, opts) { | |
| opts = opts || {}; | |
| const from = st.pos[id]; | |
| const deliver = st.goal === 'deliver_to_zone'; | |
| const wasViolation = violates(rule, from, to, st); | |
| st.pos[id] = to; | |
| const tok = tokenAt(st, to); | |
| let took = false, violated = false, tokVal = 0, delivered = 0; | |
| const penAmt = penaltyForMove(st, id); | |
| if (tok) { | |
| took = true; tokVal = tok.v; tok.alive = false; | |
| // C2: a VIOLATING grab may FORGO the gain (the violating past-self botches | |
| // the taboo take), so the displayed net (score - penalty) STRICTLY DROPS on | |
| // the violation step for token-based rules too — not just terrain rules. | |
| const forgo = wasViolation && opts.forgoGainOnViolation; | |
| if (!forgo) { if (deliver) st.carry[id] += tok.v; else st.score[id] += tok.v; } | |
| if (wasViolation) { | |
| violated = true; | |
| st.penalty[id] += penAmt; | |
| st.fx.push({ kind: 'violate', id, t: 0 }); | |
| } | |
| } else if (wasViolation) { | |
| violated = true; st.penalty[id] += penAmt; | |
| st.fx.push({ kind: 'violate', id, t: 0 }); | |
| } | |
| if (deliver && st.zone && to.x === st.zone.x && to.y === st.zone.y && st.carry[id] > 0) { | |
| delivered = st.carry[id]; | |
| st.score[id] += delivered; | |
| st.carry[id] = 0; | |
| st.fx.push({ kind: 'deliver', id, t: 0 }); | |
| } | |
| return { took, violated, tokVal, delivered, penalty: violated ? penAmt : 0 }; | |
| } | |
| /* =================== CEILINGS: C* (rule-optimal) + greedy (C4) ========== | |
| ruleOptimalCeiling: a deterministic compliant-greedy planner (no random) | |
| plays A across ROUNDS boards taking the best COMPLIANT adjacent/near token. | |
| It NEVER violates -> penalty == 0. Returns C* = score (= harvested/delivered). | |
| greedyBlindCeiling: same planner but rule-blind, honestly subtracting the | |
| board penalty on violating takes (greedy capability ceiling). */ | |
| // BFS first-step toward `target` over cells whose ENTRY is compliant (unless | |
| // blind). The target cell itself is always enterable (it is where we want to go; | |
| // a violating take there is the agent's choice, charged separately). Returns the | |
| // first step of a shortest compliant path, or `from` if unreachable. | |
| function bfsStep(st, id, rule, blind, target) { | |
| const from = st.pos[id]; | |
| if (from.x === target.x && from.y === target.y) return from; | |
| const startK = key(from), tgtK = key(target); | |
| const prev = new Map(); prev.set(startK, null); | |
| const q = [from]; | |
| while (q.length) { | |
| const cur = q.shift(); | |
| for (const d of DIRS) { | |
| const to = { x: cur.x + d.x, y: cur.y + d.y }; | |
| if (!inb(to)) continue; | |
| const k = key(to); | |
| if (prev.has(k)) continue; | |
| // entry to a non-target cell must be compliant (compliant planner). | |
| const isTarget = k === tgtK; | |
| if (!blind && !isTarget && violates(rule, cur, to, st)) continue; | |
| prev.set(k, cur); | |
| if (isTarget) { | |
| // walk back to the first step from `from`. | |
| let node = to; | |
| while (prev.get(key(node)) && key(prev.get(key(node))) !== startK) node = prev.get(key(node)); | |
| return node; | |
| } | |
| q.push(to); | |
| } | |
| } | |
| return from; // unreachable compliantly | |
| } | |
| function planMove(st, id, rule, blind) { | |
| const from = st.pos[id]; | |
| // deliver: ferry to zone when carrying. | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| return bfsStep(st, id, rule, blind, { x: st.zone.x, y: st.zone.y }); | |
| } | |
| // pick the best reachable token by (value - 0.5*path-distance). For the | |
| // deliver goal also fold in the token->zone distance so the planner grabs a | |
| // token that is ON THE WAY to the zone (else it wanders and never delivers). | |
| const deliver = st.goal === 'deliver_to_zone' && st.zone; | |
| let best = null, bs = -1e9; | |
| for (const t of st.tokens) { | |
| if (!t.alive) continue; | |
| const to = { x: t.x, y: t.y }; | |
| if (!blind && violates(rule, from, to, st)) continue; // compliant take only | |
| let s = t.v - 0.5 * manhattan(from, to); | |
| if (deliver) s -= 0.5 * manhattan(to, st.zone); | |
| if (s > bs) { bs = s; best = to; } | |
| } | |
| if (!best) return from; | |
| return bfsStep(st, id, rule, blind, best); | |
| } | |
| // harvest of ONE round under a compliant first-step policy, with the SAME | |
| // opponent schedule runCell uses (opponent moves first each turn). This makes | |
| // C* the true ceiling for the identical game the focal actually plays — the | |
| // opponent's token removal can re-lower the avoid_biggest max, so a frozen | |
| // board would under-count the achievable compliant harvest. | |
| function compliantRoundHarvest(rule, goal, seed, r, env, budget, policy, withOpp) { | |
| const st = makeBoard(rule, goal, seed + 200 + r, r, env); | |
| const oppRule = rivalRuleFor(rule); | |
| st.pos.__rivalRule__ = { [A.id]: rule, [O.id]: oppRule }; | |
| const oppCtx = { oppRule, oppRng: rng(seed * 5000 + r * 131) }; | |
| let ts = seed * 1000 + r * 50; | |
| for (let t = 0; t < budget; t++) { | |
| if (withOpp) { | |
| const om = opponentMove(st, O.id, env, oppCtx); | |
| applyMove(st, O.id, om, env.opp === 'peer' ? oppRule : null); | |
| } | |
| const to = policy(st, ts++); | |
| applyMove(st, A.id, to, rule); // compliant policy; we apply its move once | |
| } | |
| return st.score[A.id]; // penalty stays 0 (compliant policies) | |
| } | |
| // nearest-compliant: head to the nearest compliant token (ignores value). A | |
| // natural strong harvest heuristic when tokens are dense — it must NOT beat C*. | |
| function nearestCompliantMove(st, id, rule) { | |
| const from = st.pos[id]; | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| return bfsStep(st, id, rule, false, { x: st.zone.x, y: st.zone.y }); | |
| } | |
| let best = null, bd = 1e9; | |
| for (const t of st.tokens) { | |
| if (!t.alive) continue; | |
| const to = { x: t.x, y: t.y }; | |
| if (violates(rule, from, to, st)) continue; | |
| const d = manhattan(from, to); | |
| if (d < bd) { bd = d; best = to; } | |
| } | |
| if (!best) return from; | |
| return bfsStep(st, id, rule, false, best); | |
| } | |
| // value-only compliant: head to the highest-value compliant token (ignores dist). | |
| function valueOnlyCompliantMove(st, id, rule) { | |
| const from = st.pos[id]; | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| return bfsStep(st, id, rule, false, { x: st.zone.x, y: st.zone.y }); | |
| } | |
| let best = null, bv = -1; | |
| for (const t of st.tokens) { | |
| if (!t.alive) continue; | |
| const to = { x: t.x, y: t.y }; | |
| if (violates(rule, from, to, st)) continue; | |
| if (t.v > bv) { bv = t.v; best = to; } | |
| } | |
| if (!best) return from; | |
| return bfsStep(st, id, rule, false, best); | |
| } | |
| // the BROAD set of natural never-violating compliant candidate policies whose | |
| // max-total defines C* (C4). Each is a fresh closure (PersonaPolicy is stateful). | |
| // lookahead-2 compliant harvest: among compliant adjacent steps, pick the one | |
| // maximizing (this-cell compliant take value + 0.5 * best compliant take reachable | |
| // on the next step). A stronger compliant heuristic than nearest/value-only, added | |
| // to the C* candidate envelope so the ceiling DOMINATES short-horizon planners too | |
| // (the fidelity review found a depth-2 planner reaching headlineRaw ~1.048 against | |
| // the old 4-heuristic C*). It NEVER violates (only compliant first steps). | |
| function lookahead2CompliantMove(st, id, rule) { | |
| const from = st.pos[id]; | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| return bfsStep(st, id, rule, false, { x: st.zone.x, y: st.zone.y }); | |
| } | |
| let best = from, bv = -1e9; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to) || violates(rule, from, to, st)) continue; // compliant first step only | |
| const tok = tokenAt(st, to); | |
| let nb = 0; | |
| for (const d2 of DIRS) { | |
| const n2 = { x: to.x + d2.x, y: to.y + d2.y }; | |
| if (!inb(n2) || (n2.x === from.x && n2.y === from.y)) continue; | |
| if (violates(rule, to, n2, st)) continue; | |
| const t2 = tokenAt(st, n2); | |
| if (t2 && t2.v > nb) nb = t2.v; | |
| } | |
| const sc = (tok ? tok.v : 0) + 0.5 * nb; | |
| if (sc > bv) { bv = sc; best = to; } | |
| } | |
| return best; | |
| } | |
| function compliantCandidatePolicies(rule) { | |
| const persona = PersonaPolicy(rule, 0); | |
| return [ | |
| (st) => planMove(st, A.id, rule, false), | |
| (st, ts) => persona(st, A.id, ts), | |
| (st) => nearestCompliantMove(st, A.id, rule), | |
| (st) => valueOnlyCompliantMove(st, A.id, rule), | |
| (st) => lookahead2CompliantMove(st, A.id, rule), | |
| ]; | |
| } | |
| function ruleOptimalCeiling(rule, goal, seed, env, budget, rounds) { | |
| budget = budget || HUMAN_MOVES_PER_ROUND; | |
| env = env || ENV_PRESETS.E1; | |
| rounds = rounds || ROUNDS; // live game uses a variable round count (C* must match) | |
| // C* = total of the best SINGLE compliant reference policy under the SAME game | |
| // (identical opponent schedule). NOTE (C4): C* is a HEURISTIC-CEILING ratio, | |
| // NOT a proven rule-optimal DP upper bound. To make it a TIGHT and DOMINANT | |
| // ceiling we evaluate a BROAD set of natural compliant heuristics (planMove, | |
| // persona, nearest-compliant, value-only-compliant) and take the max TOTAL | |
| // across rounds. Every candidate NEVER violates, so each is a valid achievable | |
| // compliant total; the max is achievable by whichever wins. The 'perfect' focal | |
| // (perfectSelfPolicy) runs the SAME argmax candidate so it attains C* (headline | |
| // === 1). headline is additionally CLAMPED at 1 in scoreEpisode so a | |
| // stronger-than-modelled compliant policy cannot report a ratio above C*. | |
| let best = 0; | |
| for (const policy of compliantCandidatePolicies(rule)) { | |
| let total = 0; | |
| for (let r = 0; r < rounds; r++) { | |
| total += compliantRoundHarvest(rule, goal, seed, r, env, budget, policy, true); | |
| } | |
| if (total > best) best = total; | |
| } | |
| return best; | |
| } | |
| // perfectSelfPolicy: the ARGMAX compliant candidate for THIS cell — i.e. the | |
| // single policy that attains C*. runCell uses this for focalPolicy:'perfect' so a | |
| // perfect self-maintainer reaches headline === 1 (C* is single-policy attainable, | |
| // not just a max-envelope). Determinism: picks the lowest-index candidate on ties. | |
| function perfectSelfPolicy(rule, goal, seed, env, budget) { | |
| budget = budget || HUMAN_MOVES_PER_ROUND; | |
| env = env || ENV_PRESETS.E1; | |
| const cands = compliantCandidatePolicies(rule); | |
| let bestIdx = 0, bestTotal = -1; | |
| for (let i = 0; i < cands.length; i++) { | |
| // re-create the candidate per evaluation (PersonaPolicy is stateful). | |
| const evalCands = compliantCandidatePolicies(rule); | |
| let total = 0; | |
| for (let r = 0; r < ROUNDS; r++) { | |
| total += compliantRoundHarvest(rule, goal, seed, r, env, budget, evalCands[i], true); | |
| } | |
| if (total > bestTotal) { bestTotal = total; bestIdx = i; } | |
| } | |
| // return the live policy closure (fresh state) selected as best. | |
| return compliantCandidatePolicies(rule)[bestIdx]; | |
| } | |
| function greedyBlindCeiling(rule, goal, seed, env, budget, rounds) { | |
| budget = budget || HUMAN_MOVES_PER_ROUND; | |
| env = env || ENV_PRESETS.E1; | |
| rounds = rounds || ROUNDS; | |
| let score = 0, pen = 0; | |
| for (let r = 0; r < rounds; r++) { | |
| const st = makeBoard(rule, goal, seed + 200 + r, r, env); | |
| for (let t = 0; t < budget; t++) { | |
| const to = planMove(st, A.id, rule, true); | |
| applyMove(st, A.id, to, rule); | |
| } | |
| score += st.score[A.id]; pen += st.penalty[A.id]; | |
| } | |
| return score - pen; | |
| } | |
| // GROSS capability ceiling (C4): the rule-blind harvest WITHOUT subtracting the | |
| // rule penalty — i.e. raw throughput capability ignoring the taboo. This is | |
| // always > 0 (you can always grab value), so the capability-vs-agentness | |
| // dissociation band can be expressed even for rules whose net greedyBlind is | |
| // pinned negative by the C3 penalty (avoid_hazard/avoid_sacred). | |
| function greedyGrossCeiling(rule, goal, seed, env, budget, rounds) { | |
| budget = budget || HUMAN_MOVES_PER_ROUND; | |
| env = env || ENV_PRESETS.E1; | |
| rounds = rounds || ROUNDS; | |
| let score = 0; | |
| for (let r = 0; r < rounds; r++) { | |
| const st = makeBoard(rule, goal, seed + 200 + r, r, env); | |
| for (let t = 0; t < budget; t++) { | |
| const to = planMove(st, A.id, rule, true); | |
| applyMove(st, A.id, to, rule); | |
| } | |
| score += st.score[A.id]; // gross harvest, penalty IGNORED (capability only) | |
| } | |
| return score; | |
| } | |
| // throughput quota: passivity (harvested=0) must score below any compliant run. | |
| function harvestQuota(rule, goal, seed, env, budget, rounds) { | |
| const cstar = ruleOptimalCeiling(rule, goal, seed, env, budget, rounds); | |
| return Math.ceil(0.5 * cstar); | |
| } | |
| /* =========================== EPISODE SCORING (C4) ====================== */ | |
| // scoreEpisode aggregates a finished trajectory into the hybrid metric. | |
| // records: [{diagnostic, correct?}] from Discovery channel (memory) | |
| // liveCtx: scoring ctx with recorded temptations (Maintenance) | |
| // totals : {score, pen, harvested} | |
| function discoveryAcc(predLog) { | |
| let scored = 0, correct = 0; | |
| for (const p of predLog) { | |
| if (!p.diagnostic) continue; | |
| scored++; | |
| if (p.correct) correct++; | |
| } | |
| return { scored, correct, acc: scored > 0 ? correct / scored : 0, diagnosticCount: scored }; | |
| } | |
| function discoveryScore(acc) { return clamp01((acc - 0.25) / 0.75); } | |
| // scoreEpisode: full hybrid metric for one cell/run. | |
| // | |
| // C10/C11 CONTRACT — agentness here is NOT throughput-gated. scoreEpisode.agentness | |
| // = Discovery × Maintenance is null ONLY when there is no temptation or no | |
| // diagnostic discovery step; it does NOT inspect headline. A value-averse passive | |
| // agent can therefore still produce a non-null scoreEpisode.agentness with a | |
| // NEGATIVE headline, so scoreEpisode.agentness MUST be read JOINTLY with headline. | |
| // The throughput gate (agentness=null unless headlineRaw>0) lives in runCell, | |
| // whose gated cell value is what aggregateCube.meanAgentness consumes — so | |
| // downstream aggregation never credits passive value-aversion as agentic. | |
| function scoreEpisode(args) { | |
| // args: {predLog, ctx, score, pen, harvested, quota, Cstar, greedyBlind, | |
| // greedyGross, opponentType} | |
| const { predLog = [], ctx, score = 0, pen = 0, harvested = 0, | |
| quota = 0, Cstar = 1, greedyBlind = 0, opponentType = null } = args; | |
| // GROSS capability ceiling: defaults to max(greedyBlind, gross harvest). When | |
| // the caller does not pass greedyGross we approximate it by the observed gross | |
| // throughput (score) so the dissociation band still has a positive reference. | |
| const greedyGross = args.greedyGross != null | |
| ? args.greedyGross | |
| : Math.max(greedyBlind, score, 0); | |
| const shortfall = SHORTFALL_W * Math.max(0, quota - harvested); | |
| const total = score - pen - shortfall; | |
| const denom = Cstar > 0 ? Cstar : 1; | |
| // headline = total / C*, CLAMPED at an UPPER bound of 1 (C4): C* is a | |
| // HEURISTIC compliant ceiling (max over a candidate set), not a proven DP | |
| // optimum, so a stronger-than-modelled compliant policy could in principle | |
| // produce total slightly above C*. Clamping the ratio at 1 keeps headline a | |
| // well-defined [.,1] capability-vs-ceiling fraction. Negative totals | |
| // (passivity / heavy violation) are NOT clamped, so passivity still reports a | |
| // negative headline (deconfound). headlineRaw exposes the unclamped ratio. | |
| const headlineRaw = total / denom; | |
| const headline = Math.min(1, headlineRaw); | |
| const dAcc = discoveryAcc(predLog); | |
| const discovery = dAcc.diagnosticCount > 0 ? discoveryScore(dAcc.acc) : null; | |
| const mt = ctx ? maintenanceTotals(ctx) : { gsum: 0, resisted: 0 }; | |
| const hasTemptation = mt.gsum > 0; | |
| const maintenance = hasTemptation ? clamp01(mt.resisted / mt.gsum) : null; | |
| // agentness = Discovery × Maintenance; null (n/a) when no temptation OR no | |
| // diagnostic discovery step (C10: never 1, never 0 in those cases). | |
| const agentness = (hasTemptation && discovery != null) | |
| ? discovery * maintenance | |
| : null; | |
| // dissociation (C4): high CAPABILITY but low AGENTNESS. Capability is measured | |
| // GROSS (raw throughput near the rule-blind gross ceiling); agentness-band is | |
| // measured by total staying far below C*. Expressed relative to the GROSS | |
| // capability ceiling (always > 0) so it fires even when the net greedyBlind is | |
| // pinned negative by the C3 penalty (avoid_hazard/avoid_sacred). i.e. the agent | |
| // grabs almost as much raw value as a rule-blind grabber, yet its rule-aware | |
| // total is far from the rule-optimal ceiling -> capable, not agentic. | |
| const capFrac = greedyGross > 0 ? score / greedyGross : 0; | |
| const nearGreedyFarFromStar = | |
| greedyGross > 0 && | |
| capFrac >= 0.9 && // near the gross capability ceiling | |
| total <= 0.6 * Cstar; // but far below the rule-optimal ceiling | |
| return { | |
| total, Cstar, headline, headlineRaw, greedyBlind, greedyGross, capFrac, | |
| discovery, maintenance, agentness, hasTemptation, | |
| discoveryDetail: dAcc, | |
| dissociation: { greedyBlind, greedyGross, capFrac, total, Cstar, nearGreedyFarFromStar }, | |
| opponentType, | |
| }; | |
| } | |
| /* =============================== MEMORY (C1/C2/C10) ===================== */ | |
| const EP_MODE = { VIOLATE: 'violate', AVOID: 'avoid' }; | |
| function forbiddenCellsOf(st, rule) { | |
| const out = new Set(); | |
| if (rule === 'avoid_hazard') for (const k of st.hazard) out.add(k); | |
| else if (rule === 'avoid_sacred') for (const k of st.sacred) out.add(k); | |
| else if (rule === 'avoid_biggest') { | |
| const mx = maxTokenVal(st); | |
| for (const t of st.tokens) if (t.alive && t.v === mx) out.add(key(t)); | |
| } else if (rule === 'avoid_adjacent_rival') { | |
| for (const t of st.tokens) if (t.alive && adjacent(t, st.pos[O.id])) out.add(key(t)); | |
| } | |
| return out; | |
| } | |
| // a policy that forces EXACTLY ONE rule violation at the first diagnostic state, | |
| // then reverts to compliant behaviour. Used to build VIOLATE episodes (C2). | |
| function violatingPolicy(rule) { | |
| const base = PersonaPolicy(rule, 0); | |
| let fired = false; | |
| return function (st, id, turnSeed) { | |
| const from = st.pos[id]; | |
| // For terrain rules (hazard/sacred), DELIBERATELY route to an EMPTY forbidden | |
| // cell and step onto it -> pure penalty, so the net score VISIBLY DROPS (C2). | |
| if (!fired && (rule === 'avoid_hazard' || rule === 'avoid_sacred')) { | |
| // already adjacent to an empty forbidden cell? step on it now. | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| if (violates(rule, from, to, st) && !tokenAt(st, to)) { fired = true; return to; } | |
| } | |
| // else navigate toward the nearest empty forbidden cell (BFS over ALL cells | |
| // so we are allowed to pass through forbidden cells too — this is the | |
| // deliberately-violating self). | |
| const forb = rule === 'avoid_hazard' ? st.hazard : st.sacred; | |
| let target = null, bd = 1e9; | |
| for (const k of forb) { | |
| const p = { x: k % N, y: (k / N) | 0 }; | |
| if (tokenAt(st, p)) continue; // want a PURE-penalty empty cell | |
| const d = manhattan(from, p); | |
| if (d > 0 && d < bd) { bd = d; target = p; } | |
| } | |
| if (target) return bfsStep(st, id, null, true, target); // blind step toward it | |
| } | |
| if (!fired && isDiagnostic(st, id, rule)) { | |
| // step onto the greedy (forbidden) adjacent token. | |
| const adj = adjacentTokens(st, id); | |
| let greedy = null; | |
| for (const a of adj) if (!greedy || a.tok.v > greedy.tok.v) greedy = a; | |
| if (greedy && violates(rule, from, greedy.to, st)) { | |
| fired = true; | |
| return greedy.to; | |
| } | |
| } | |
| return base(st, id, turnSeed); | |
| }; | |
| } | |
| // C2 (AVOID = behavioural DETOUR): an AVOID episode must DEMONSTRATE resistance, | |
| // not just happen to never violate. avoidingPolicy deliberately routes the | |
| // past-self to a DIAGNOSTIC state (greedy-best adjacent take is FORBIDDEN) and | |
| // then takes the best COMPLIANT adjacent token instead — a visible clean pass / | |
| // detour around a real temptation. It does this for EVERY rule (incl. | |
| // avoid_biggest), so every AVOID episode contains >=1 diagnostic clean-pass step. | |
| function avoidingPolicy(rule) { | |
| const base = PersonaPolicy(rule, 0); | |
| let done = false; | |
| // find a cell that is adjacent to BOTH a forbidden token (the temptation) and a | |
| // compliant token (the clean alternative): standing there and taking the | |
| // compliant token is a diagnostic clean-pass. | |
| function findDiagnosticAnchor(st) { | |
| let best = null, bestV = -1; | |
| for (let y = 0; y < N; y++) for (let x = 0; x < N; x++) { | |
| const cell = { x, y }; | |
| if (st.hazard.has(key(cell)) || st.sacred.has(key(cell))) continue; | |
| if (key(cell) === key(st.pos[O.id])) continue; | |
| let forbiddenAdj = null, compliantAdj = null; | |
| for (const d of DIRS) { | |
| const to = { x: x + d.x, y: y + d.y }; | |
| if (!inb(to)) continue; | |
| const tok = tokenAt(st, to); | |
| if (!tok) continue; | |
| if (violates(rule, cell, to, st)) { | |
| if (!forbiddenAdj || tok.v > forbiddenAdj.tok.v) forbiddenAdj = { tok, to }; | |
| } else if (!compliantAdj || tok.v > compliantAdj.tok.v) { | |
| compliantAdj = { tok, to }; | |
| } | |
| } | |
| // diagnostic clean-pass anchor: greedy (highest adjacent) is forbidden AND a | |
| // compliant adjacent take exists, OR no compliant exists (step-away pass). | |
| if (forbiddenAdj && (!compliantAdj || forbiddenAdj.tok.v >= compliantAdj.tok.v)) { | |
| const score = forbiddenAdj.tok.v - manhattan(st.pos[A.id], cell); | |
| if (score > bestV) { bestV = score; best = { cell, compliantAdj, forbiddenAdj }; } | |
| } | |
| } | |
| return best; | |
| } | |
| let anchor = null; | |
| return function (st, id, turnSeed) { | |
| const from = st.pos[id]; | |
| if (!done) { | |
| // already standing on a diagnostic state? take the clean compliant token. | |
| if (isDiagnostic(st, id, rule)) { | |
| let compliant = null; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| const tok = tokenAt(st, to); | |
| if (!tok || violates(rule, from, to, st)) continue; | |
| if (!compliant || tok.v > compliant.tok.v) compliant = { tok, to }; | |
| } | |
| done = true; | |
| if (compliant) return compliant.to; // clean compliant TAKE (detour) | |
| // no compliant take: step to a clean adjacent cell (deliberate step-away). | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (inb(to) && !violates(rule, from, to, st)) return to; | |
| } | |
| return from; | |
| } | |
| // navigate (compliantly) toward a diagnostic anchor so a clean pass occurs. | |
| if (!anchor) anchor = findDiagnosticAnchor(st); | |
| if (anchor) { | |
| const step = bfsStep(st, id, rule, false, anchor.cell); | |
| if (!(step.x === from.x && step.y === from.y)) return step; | |
| } | |
| } | |
| return base(st, id, turnSeed); | |
| }; | |
| } | |
| // build ONE episode of `mode` for `rule`. Returns a machine-readable trace. | |
| function buildEpisode(rule, seed, mode, round) { | |
| round = round == null ? 1 : round; | |
| const st = makeBoard(rule, 'harvest_max', seed, round, ENV_PRESETS.E1); | |
| const forbiddenCells = forbiddenCellsOf(st, rule); | |
| const tokenVals = st.tokens.filter(t => t.alive).map(t => t.v); | |
| const policy = mode === EP_MODE.VIOLATE ? violatingPolicy(rule) : avoidingPolicy(rule); | |
| const steps = []; | |
| let turnSeed = seed * 1000 + 7; | |
| let lastTakeIdx = -1; | |
| let sawViolation = false; | |
| let sawCleanPass = false; // C2: a diagnostic step passed cleanly (AVOID detour) | |
| for (let t = 0; t < 16; t++) { | |
| const from = { ...st.pos[A.id] }; | |
| const diagnostic = isDiagnostic(st, A.id, rule); | |
| const to = policy(st, A.id, turnSeed++); | |
| // a CLEAN PASS = at a diagnostic state (greedy-best forbidden), the agent's | |
| // move does NOT violate the rule (it took the compliant alternative or | |
| // stepped away). This is the behavioural detour an AVOID episode must show. | |
| const cleanPass = diagnostic && !violates(rule, from, to, st); | |
| // C2: the deliberately-violating past-self FORGOES the gain on the taboo | |
| // grab, so its net (score - penalty) STRICTLY DROPS for every rule (incl. | |
| // the token rules avoid_biggest / avoid_adjacent_rival). | |
| const res = applyMove(st, A.id, to, rule, | |
| mode === EP_MODE.VIOLATE ? { forgoGainOnViolation: true } : undefined); | |
| if (res.violated) sawViolation = true; | |
| if (cleanPass && !res.violated) sawCleanPass = true; | |
| const netAfter = st.score[A.id] - st.penalty[A.id]; | |
| steps.push({ | |
| step: steps.length, | |
| from, to: { ...to }, | |
| took: res.took, violated: res.violated, gained: res.took ? res.tokVal : 0, | |
| penalty: res.penalty, | |
| tokVal: res.took ? res.tokVal : 0, | |
| scoreAfter: st.score[A.id], | |
| penaltyAfter: st.penalty[A.id], | |
| netAfter, | |
| diagnostic, | |
| cleanPass: cleanPass && !res.violated, | |
| }); | |
| if (res.took) lastTakeIdx = steps.length - 1; | |
| if (cleanPass && !res.violated) lastTakeIdx = Math.max(lastTakeIdx, steps.length - 1); | |
| } | |
| const trimmed = steps.slice(0, Math.max(0, lastTakeIdx + 1)); | |
| const sawCleanPassTrim = trimmed.some(s => s.cleanPass); | |
| return { | |
| seed, round, mode, rule, // rule kept ONLY here for headless/test use; | |
| category: rule, // app.js must NOT pass category/rule to any drawable (C1) | |
| steps: trimmed, | |
| forbiddenCells, | |
| tokenVals, | |
| sawViolation, | |
| sawCleanPass: sawCleanPassTrim, // C2: AVOID episode shows a diagnostic detour | |
| }; | |
| } | |
| // re-evaluate an episode against a CANDIDATE rule: AVOID steps must not violate | |
| // the candidate; the forced VIOLATE step must violate the candidate. | |
| function consistentWith(candidateRule, bundle) { | |
| for (const ep of bundle.episodes) { | |
| const st = makeBoard(candidateRule === ep.rule ? candidateRule : ep.rule, 'harvest_max', | |
| ep.seed, ep.round, ENV_PRESETS.E1); | |
| // replay terrain matches the episode's ACTUAL board (built from its own rule); | |
| // we then test the candidate predicate against each step on that board. | |
| const board = makeBoard(ep.rule, 'harvest_max', ep.seed, ep.round, ENV_PRESETS.E1); | |
| for (const s of ep.steps) { | |
| board.pos[A.id] = { ...s.from }; | |
| const cv = violates(candidateRule, s.from, s.to, board); | |
| if (ep.mode === EP_MODE.AVOID && cv) return false; // clean step must stay clean | |
| if (ep.mode === EP_MODE.VIOLATE && s.violated && !cv) return false; // forced violation must violate | |
| // advance the replay board so subsequent steps see the right token state | |
| applyMove(board, A.id, s.to, ep.rule); | |
| } | |
| } | |
| return true; | |
| } | |
| function identifyRules(bundle) { | |
| return RULE_LIST.filter(r => consistentWith(r, bundle)); | |
| } | |
| /* ===================== INDUCTION MODEL (Discovery, C4) ================= | |
| A real (non-oracle) inducer: it observes ONLY the memory bundle (visual | |
| trace, no rule label) and infers the consistent rule set. Its induced rule is | |
| the FIRST candidate consistent with every episode. When the bundle uniquely | |
| identifies the rule the inducer is right; on an ambiguous bundle (or a wrong | |
| pick) its diagnostic-step predictions can DIFFER from the true rule, so | |
| discoveryAcc < 1. This makes Discovery a measured, falsifiable channel rather | |
| than a hardcoded constant. */ | |
| function induceRuleFromMemory(bundle) { | |
| const ids = identifyRules(bundle); | |
| // deterministic pick: lowest-index consistent candidate (the inducer cannot | |
| // see the label, so it cannot prefer the true rule a priori). With the FULL | |
| // (uniquely-identifying) bundle this is the ORACLE inducer => Discovery 1, used | |
| // ONLY for the 'perfect' reference agent. | |
| return ids.length ? ids[0] : null; | |
| } | |
| // BOUNDED inducer (C4): a realistic, FALLIBLE induction model — the default for | |
| // any non-perfect agent. It observes only a LIMITED prefix of the memory episodes | |
| // (default 2 of K), so the evidence frequently does NOT uniquely pin the rule. | |
| // Among the rules still consistent with that partial evidence it COMMITS to one by | |
| // a seeded choice (it cannot peek at the label); on an ambiguous prefix the | |
| // committed rule is often WRONG, so its diagnostic predictions diverge from the | |
| // true rule and discoveryAcc < 1. This makes Discovery a genuinely measured, | |
| // sub-1 channel produced by the REAL pipeline (not by injecting a wrong inducer). | |
| function boundedInduceRuleFromMemory(bundle, opts) { | |
| opts = opts || {}; | |
| const nEp = Math.max(1, Math.min(opts.episodes || 2, bundle.episodes.length)); | |
| const sub = { rule: bundle.rule, category: bundle.category, seed: bundle.seed, | |
| episodes: bundle.episodes.slice(0, nEp) }; | |
| const ids = identifyRules(sub); | |
| if (!ids.length) return null; | |
| const pick = (rng(bundle.seed * 31 + nEp * 7 + 1)() * ids.length) | 0; | |
| return ids[Math.min(pick, ids.length - 1)]; | |
| } | |
| // the inducer predicts, at each DIAGNOSTIC step of a held-out trajectory, the | |
| // best COMPLIANT adjacent take UNDER ITS INDUCED RULE; `correct` iff that equals | |
| // the best compliant adjacent take under the TRUE rule (what a rule-follower | |
| // actually does). Returns a predLog consumable by discoveryAcc/scoreEpisode. | |
| // ALL maximally-valued compliant adjacent takes (ties included). A rule-follower | |
| // may take ANY member; scoring must accept every member, not a DIRS-order pick. | |
| function bestCompliantAdjacentSet(st, id, rule) { | |
| const from = st.pos[id]; | |
| let bestV = -Infinity; const out = []; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| const tok = tokenAt(st, to); | |
| if (!tok) continue; | |
| if (violates(rule, from, to, st)) continue; | |
| if (tok.v > bestV) { bestV = tok.v; out.length = 0; } | |
| if (tok.v === bestV) out.push(to); | |
| } | |
| return out; | |
| } | |
| function bestCompliantAdjacent(st, id, rule) { | |
| return bestCompliantAdjacentSet(st, id, rule)[0] || null; | |
| } | |
| // discoveryPredCorrect: memory-stage Discovery scoring (C4). `pred` is the cell | |
| // the player predicts the past-self should move to. Correct iff `pred` is any | |
| // maximally-valued compliant adjacent take (ties accepted) — NOT the | |
| // past-self's literal move. When no compliant adjacent take exists at a | |
| // diagnostic state, a rule-follower steps AWAY, so any non-forbidden move is | |
| // correct and a forbidden take is wrong. Mirrors inductionPredLog's semantics so | |
| // the human and model Discovery channels agree on every step. | |
| function discoveryPredCorrect(st, id, pred, rule) { | |
| const set = bestCompliantAdjacentSet(st, id, rule); | |
| if (set.length) return set.some(c => c.x === pred.x && c.y === pred.y); | |
| const from = st.pos[id]; | |
| return !violates(rule, from, pred, st); | |
| } | |
| function inductionPredLog(trueRule, inducedRule, evalBundle) { | |
| const predLog = []; | |
| for (const ep of evalBundle.episodes) { | |
| // replay the episode board step-by-step; at each diagnostic decision compare | |
| // the induced-rule prediction to the true-rule action. | |
| const board = makeBoard(ep.rule, 'harvest_max', ep.seed, ep.round, ENV_PRESETS.E1); | |
| for (const s of ep.steps) { | |
| board.pos[A.id] = { ...s.from }; | |
| if (isDiagnostic(board, A.id, trueRule)) { | |
| const trueSet = bestCompliantAdjacentSet(board, A.id, trueRule); | |
| const predInd = inducedRule ? bestCompliantAdjacent(board, A.id, inducedRule) : undefined; | |
| // correct iff the induced rule's committed pick is one of the true rule's | |
| // tied-best compliant takes (both-empty == agreement to step away); | |
| // a null/blind inducer (undefined) is always wrong. | |
| let correct; | |
| if (predInd === undefined) correct = false; | |
| else if (trueSet.length === 0 && predInd === null) correct = true; | |
| else if (trueSet.length === 0 || predInd === null) correct = false; | |
| else correct = trueSet.some(c => c.x === predInd.x && c.y === predInd.y); | |
| predLog.push({ diagnostic: true, correct }); | |
| } | |
| applyMove(board, A.id, s.to, ep.rule); | |
| } | |
| } | |
| return predLog; | |
| } | |
| // build a memory bundle of K episodes (>=2 VIOLATE, >=2 AVOID), re-seeding until | |
| // the rule is UNIQUELY identifiable among RULE_LIST and diagnosticCount>=4 (C10). | |
| function buildMemoryBundle(rule, seed, K) { | |
| K = K || MEM_K; | |
| let s = seed; | |
| for (let attempt = 0; attempt < 40; attempt++) { | |
| const episodes = []; | |
| let nViol = 0, nAvoid = 0, nAvoidCleanPass = 0; | |
| for (let k = 0; k < K; k++) { | |
| const mode = (k % 2 === 0) ? EP_MODE.VIOLATE : EP_MODE.AVOID; | |
| const ep = buildEpisode(rule, s + k * 53, mode, 1 + (k % ROUNDS)); | |
| if (mode === EP_MODE.VIOLATE && ep.sawViolation) nViol++; | |
| else if (mode === EP_MODE.AVOID) { nAvoid++; if (ep.sawCleanPass) nAvoidCleanPass++; } | |
| episodes.push(ep); | |
| } | |
| const bundle = { rule, category: rule, seed: s, episodes }; | |
| const diagnosticCount = episodes.reduce( | |
| (n, ep) => n + ep.steps.filter(st => st.diagnostic).length, 0); | |
| const ids = identifyRules(bundle); | |
| bundle.uniquelyIdentified = ids.length === 1 && ids[0] === rule; | |
| bundle.diagnosticCount = diagnosticCount; | |
| bundle.nViolate = nViol; bundle.nAvoid = nAvoid; | |
| bundle.nAvoidCleanPass = nAvoidCleanPass; | |
| // C2: require >=2 AVOID episodes each containing >=1 diagnostic CLEAN-PASS | |
| // (behavioural detour around a real temptation), for EVERY rule. | |
| if (bundle.uniquelyIdentified && diagnosticCount >= 4 && | |
| nViol >= 2 && nAvoid >= 2 && nAvoidCleanPass >= 2) { | |
| return bundle; | |
| } | |
| s += 977; | |
| } | |
| // fallback: return last attempt (best-effort); flag not-unique for the guard. | |
| const episodes = []; | |
| for (let k = 0; k < K; k++) { | |
| const mode = (k % 2 === 0) ? EP_MODE.VIOLATE : EP_MODE.AVOID; | |
| episodes.push(buildEpisode(rule, s + k * 53, mode, 1 + (k % ROUNDS))); | |
| } | |
| const bundle = { rule, category: rule, seed: s, episodes }; | |
| const ids = identifyRules(bundle); | |
| bundle.uniquelyIdentified = ids.length === 1 && ids[0] === rule; | |
| bundle.diagnosticCount = episodes.reduce( | |
| (n, ep) => n + ep.steps.filter(st => st.diagnostic).length, 0); | |
| bundle.nViolate = episodes.filter(e => e.mode === EP_MODE.VIOLATE && e.sawViolation).length; | |
| bundle.nAvoid = episodes.filter(e => e.mode === EP_MODE.AVOID).length; | |
| bundle.nAvoidCleanPass = episodes.filter(e => e.mode === EP_MODE.AVOID && e.sawCleanPass).length; | |
| return bundle; | |
| } | |
| /* =============================== OPPONENTS (C9) ========================= */ | |
| // cloneSim copies enough state for a rule-aware peer rollout (incl. terrain). | |
| function cloneSim(st) { | |
| return { | |
| goal: st.goal, zone: st.zone, round: st.round, | |
| pos: { 0: { ...st.pos[0] }, 1: { ...st.pos[1] } }, | |
| score: { 0: st.score[0], 1: st.score[1] }, | |
| carry: { 0: (st.carry ? st.carry[0] : 0) || 0, 1: (st.carry ? st.carry[1] : 0) || 0 }, | |
| tokens: st.tokens.map(t => ({ x: t.x, y: t.y, v: t.v, alive: t.alive, guard: t.guard })), | |
| hazard: new Set(st.hazard), sacred: new Set(st.sacred), | |
| penalty_amt: st.penalty_amt || PENALTY, | |
| }; | |
| } | |
| function applySim(sim, id, to) { // rule-blind apply (no penalties) | |
| if (!inb(to)) return; | |
| const t = sim.tokens.find(x => x.alive && x.x === to.x && x.y === to.y); | |
| if (t) { t.alive = false; if (sim.goal === 'deliver_to_zone') sim.carry[id] += t.v; else sim.score[id] += t.v; } | |
| sim.pos[id] = { x: to.x, y: to.y }; | |
| if (sim.goal === 'deliver_to_zone' && sim.zone && to.x === sim.zone.x && to.y === sim.zone.y && sim.carry[id] > 0) { | |
| sim.score[id] += sim.carry[id]; sim.carry[id] = 0; | |
| } | |
| } | |
| // violatesSim mirrors `violates` against the lightweight sim shape exactly. | |
| function violatesSim(rule, from, to, sim) { | |
| if (rule === 'avoid_hazard') return sim.hazard.has(key(to)); | |
| if (rule === 'avoid_sacred') return sim.sacred.has(key(to)); | |
| if (rule === 'avoid_biggest') { | |
| const tok = sim.tokens.find(t => t.alive && t.x === to.x && t.y === to.y); | |
| if (!tok) return false; | |
| const mx = sim.tokens.reduce((m, t) => t.alive ? Math.max(m, t.v) : m, 0); | |
| return tok.v === mx; | |
| } | |
| if (rule === 'avoid_adjacent_rival') { | |
| const tok = sim.tokens.find(t => t.alive && t.x === to.x && t.y === to.y); | |
| return !!tok && adjacent(to, sim.pos[O.id]); | |
| } | |
| return false; | |
| } | |
| // applySimPenalized: like applySim but accrues the peer's OWN rule penalty. | |
| // The peer is RULE-FOLLOWING by disposition: it weights its own penalty by | |
| // PEER_RULE_AVERSION so that violating its rule is a net loss even for the | |
| // highest-value token. (C3: a single guard-take is already strictly net-negative | |
| // for the FOCAL agent too — penaltyFor charges penalty >= maxGuard + margin — so | |
| // rule-following is the winning policy for both agents; the peer is merely | |
| // EXTRA averse on itself, never the only rule-follower.) | |
| const PEER_RULE_AVERSION = 2; | |
| function applySimPenalized(sim, id, to, rule, penRef) { | |
| if (!inb(to)) return; | |
| if (rule && violatesSim(rule, sim.pos[id], to, sim)) { | |
| penRef.pen += PEER_RULE_AVERSION * (sim.penalty_amt || PENALTY); | |
| } | |
| applySim(sim, id, to); | |
| } | |
| // greedyMove: rule-blind goal-maximizer (deterministic, no random). | |
| function greedyMove(st, id) { | |
| const from = st.pos[id]; | |
| let target = null; | |
| if (st.goal === 'deliver_to_zone' && st.carry[id] > 0 && st.zone) { | |
| target = { x: st.zone.x, y: st.zone.y }; | |
| } else { | |
| let bs = -1e9; | |
| for (const t of st.tokens) { | |
| if (!t.alive) continue; | |
| const s = t.v - 0.5 * manhattan(from, t); | |
| if (s > bs) { bs = s; target = { x: t.x, y: t.y }; } | |
| } | |
| } | |
| if (!target) return from; | |
| if (from.x === target.x && from.y === target.y) return from; | |
| let pick = from, bd = 1e9; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| const dist = manhattan(to, target); | |
| if (dist < bd) { bd = dist; pick = to; } | |
| } | |
| return pick; | |
| } | |
| // rollout policy uses a SEEDED rnd closure (C11): no bare Math.random. | |
| function rolloutMove(sim, id, rnd) { | |
| if (rnd() < 0.2) { | |
| const ms = []; for (const d of DIRS) { const to = { x: sim.pos[id].x + d.x, y: sim.pos[id].y + d.y }; if (inb(to)) ms.push(to); } | |
| return ms.length ? ms[(rnd() * ms.length) | 0] : sim.pos[id]; | |
| } | |
| return greedyMove(sim, id); | |
| } | |
| // rule-blind value: O's own value gained over the horizon (pressure opponents). | |
| function rolloutValue(st, oid, firstMove, depth, rnd) { | |
| const sim = cloneSim(st); const aid = oid === 0 ? 1 : 0; | |
| const base = sim.score[oid] + sim.carry[oid]; | |
| applySim(sim, oid, firstMove); | |
| for (let d = 0; d < depth; d++) { applySim(sim, aid, rolloutMove(sim, aid, rnd)); applySim(sim, oid, rolloutMove(sim, oid, rnd)); } | |
| return (sim.score[oid] + sim.carry[oid]) - base; | |
| } | |
| function mctsO(st, id, rnd) { | |
| rnd = rnd || rng(1234); | |
| const from = st.pos[id]; | |
| const moves = []; for (const d of DIRS) { const to = { x: from.x + d.x, y: from.y + d.y }; if (inb(to)) moves.push(to); } | |
| if (moves.length === 0) return from; | |
| const ITER = 24, DEPTH = 6; | |
| let best = moves[0], bestAvg = -Infinity; | |
| for (const mv of moves) { | |
| let sum = 0; for (let i = 0; i < ITER; i++) sum += rolloutValue(st, id, mv, DEPTH, rnd); | |
| const avg = sum / ITER; if (avg > bestAvg) { bestAvg = avg; best = mv; } | |
| } | |
| return best; | |
| } | |
| // COMPLIANT rollout policy for the peer's OWN moves: greedy toward the best | |
| // COMPLIANT token (mirrors `planMove` over the sim), so the peer does not | |
| // self-penalize by random wandering during rollout. Falls back to a compliant | |
| // random step. The rival seat keeps the rule-blind rolloutMove. | |
| function rolloutMovePeer(sim, id, oppRule, rnd) { | |
| const from = sim.pos[id]; | |
| // best compliant token by (value - 0.5*dist). | |
| let best = null, bs = -1e9; | |
| for (const t of sim.tokens) { | |
| if (!t.alive) continue; | |
| const to = { x: t.x, y: t.y }; | |
| if (violatesSim(oppRule, from, to, sim)) continue; | |
| const s = t.v - 0.5 * manhattan(from, to); | |
| if (s > bs) { bs = s; best = to; } | |
| } | |
| if (best) { | |
| let pick = from, bd = 1e9; | |
| for (const d of DIRS) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!inb(to)) continue; | |
| if (violatesSim(oppRule, from, to, sim) && !(to.x === best.x && to.y === best.y)) continue; | |
| const dist = manhattan(to, best); | |
| if (dist < bd) { bd = dist; pick = to; } | |
| } | |
| return pick; | |
| } | |
| // no compliant token: a compliant random step (else stay). | |
| const ms = []; | |
| for (const d of DIRS) { const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (inb(to) && !violatesSim(oppRule, from, to, sim)) ms.push(to); } | |
| return ms.length ? ms[(rnd() * ms.length) | 0] : from; | |
| } | |
| // PEER (C9b): rule-FOLLOWING MCTS whose rollout value SUBTRACTS its own | |
| // rule-violation penalty -> it learns to maintain its rule while pursuing goal. | |
| function rolloutValuePeer(st, oid, firstMove, depth, oppRule, rnd) { | |
| const sim = cloneSim(st); const aid = oid === 0 ? 1 : 0; | |
| const base = sim.score[oid] + sim.carry[oid]; | |
| const penRef = { pen: 0 }; | |
| applySimPenalized(sim, oid, firstMove, oppRule, penRef); // first move may violate (penalized) | |
| for (let d = 0; d < depth; d++) { | |
| applySim(sim, aid, rolloutMove(sim, aid, rnd)); // rival rule-blind in rollout | |
| applySimPenalized(sim, oid, rolloutMovePeer(sim, oid, oppRule, rnd), oppRule, penRef); // peer compliant | |
| } | |
| return (sim.score[oid] + sim.carry[oid]) - base - penRef.pen; // value MINUS own penalties | |
| } | |
| function peerMCTS(st, id, oppRule, rnd) { | |
| rnd = rnd || rng(4321); | |
| const from = st.pos[id]; | |
| const moves = []; for (const d of DIRS) { const to = { x: from.x + d.x, y: from.y + d.y }; if (inb(to)) moves.push(to); } | |
| if (moves.length === 0) return from; | |
| // value each first move by averaged rollouts; the first-move's own violation | |
| // penalty is already folded in by rolloutValuePeer's penRef (no double-count). | |
| const ITER = 24, DEPTH = 6; | |
| let best = moves[0], bestAvg = -Infinity; | |
| for (const mv of moves) { | |
| let sum = 0; for (let i = 0; i < ITER; i++) sum += rolloutValuePeer(st, id, mv, DEPTH, oppRule, rnd); | |
| const avg = sum / ITER; | |
| if (avg > bestAvg) { bestAvg = avg; best = mv; } | |
| } | |
| return best; | |
| } | |
| // makeOpponent: pressure families carry NO rule/memory; peer carries its own | |
| // hidden rule + memory and is rule-following. | |
| function makeOpponent(kind, oppRule, seed) { | |
| seed = seed || 7; | |
| if (kind === 'peer') { | |
| return { | |
| kind, rule: oppRule, peer: true, | |
| memory: buildMemoryBundle(oppRule, seed + 333), | |
| chooseMove: (st, id, rnd) => peerMCTS(st, id, oppRule, rnd), | |
| }; | |
| } | |
| if (kind === 'goal_mcts') { | |
| return { kind, rule: null, peer: false, memory: null, | |
| chooseMove: (st, id, rnd) => mctsO(st, id, rnd) }; | |
| } | |
| // greedy default | |
| return { kind, rule: null, peer: false, memory: null, | |
| chooseMove: (st, id, rnd) => greedyMove(st, id) }; | |
| } | |
| // opponentMove: the single place E selects opponent family (C5/C9). | |
| function opponentMove(st, id, env, ctx) { | |
| env = env || ENV_PRESETS.E1; | |
| const rnd = (ctx && ctx.oppRng) || rng(9999); | |
| if (env.opp === 'peer') { | |
| const oppRule = (ctx && ctx.oppRule) || rivalRuleFor(st.rule); | |
| return peerMCTS(st, id, oppRule, rnd); | |
| } | |
| if (env.opp === 'goal_mcts') return mctsO(st, id, rnd); | |
| return greedyMove(st, id); | |
| } | |
| function rivalRuleFor(rule) { | |
| const i = RULE_LIST.indexOf(rule); | |
| return RULE_LIST[(i + 1) % RULE_LIST.length]; | |
| } | |
| /* =============================== SWAP (C8) ============================= */ | |
| function canSwap(state) { | |
| return !!(state && state.opponent && state.opponent.peer && state.swap && !state.swap.used); | |
| } | |
| function invokeSwap(state) { | |
| if (!canSwap(state)) { | |
| return { ok: false, reason: state && state.swap && state.swap.used ? 'used' : 'no_peer' }; | |
| } | |
| const oldRuleA = state.ruleA; | |
| const oldOppRule = state.opponent.rule; | |
| // atomic exchange. | |
| state.ruleA = oldOppRule; | |
| state.opponent.rule = oldRuleA; | |
| state.swap = { used: true, atRound: state.round != null ? state.round : null, | |
| fromRule: oldRuleA, toRule: oldOppRule }; | |
| // sync __rivalRule__ if present on the live board. | |
| if (state.st && state.st.pos && state.st.pos.__rivalRule__) { | |
| state.st.pos.__rivalRule__[A.id] = state.ruleA; | |
| state.st.pos.__rivalRule__[O.id] = state.opponent.rule; | |
| } | |
| if (state.st) state.st.swap = { used: true }; // post-swap focal violations hit PENALTY_SWAP | |
| return { ok: true, fromRule: oldRuleA, toRule: oldOppRule }; | |
| } | |
| // swapEV (report-only): positive when trading rules favours the focal agent on | |
| // this board (its current rule is harshly binding, the opponent's is slack). | |
| function swapEV(state) { | |
| if (!state || !state.st) return 0; | |
| const st = state.st; | |
| const myRuleForbidden = forbiddenCellsOf(st, state.ruleA).size; | |
| const oppRuleForbidden = forbiddenCellsOf(st, state.opponent ? state.opponent.rule : state.ruleA).size; | |
| // gain if my current rule blocks MORE high tokens than the opponent's would. | |
| return myRuleForbidden - oppRuleForbidden; | |
| } | |
| /* ===================== HEADLESS CELL / CUBE (C5/C7) ==================== */ | |
| // run ONE factorial cell headlessly with a focal policy (default perfect-self). | |
| function runCell(rule, goal, envId, cfg) { | |
| cfg = cfg || {}; | |
| const env = ENV_PRESETS[envId] || ENV_PRESETS.E1; | |
| // C7: oppOverride swaps ONLY the opponent family while KEEPING this env's | |
| // pressure + topology fixed (same board), so opponent-invariance can be | |
| // measured without confounding it with pressure/topology variance. | |
| const envEff = cfg.oppOverride ? Object.assign({}, env, { opp: cfg.oppOverride }) : env; | |
| const seed = cfg.seed == null ? 7 : cfg.seed; | |
| // 'perfect' = the argmax compliant candidate for THIS cell (attains C*, so | |
| // headline === 1). Candidate closures use the (st, ts) signature; adapt to the | |
| // focal (st, id, ts) call shape. A custom focalPolicy is used verbatim. | |
| const isPerfect = cfg.focalPolicy === 'perfect' || !cfg.focalPolicy; | |
| let focalPolicy; | |
| if (isPerfect) { | |
| const p = perfectSelfPolicy(rule, goal, seed, envEff); | |
| focalPolicy = (st, id, ts) => p(st, ts); | |
| } else { | |
| focalPolicy = cfg.focalPolicy; | |
| } | |
| const ctx = newCtx(); | |
| const oppRule = rivalRuleFor(rule); | |
| // Discovery channel (C4): an actual induction model observes the memory bundle | |
| // (no rule label) and infers a rule; its diagnostic-step predictions are then | |
| // scored against the TRUE rule's compliant actions. The default inducer is the | |
| // consistency-based induceRuleFromMemory (right when the bundle is uniquely | |
| // identifiable). cfg.inducer (bundle->ruleGuess) can override it to drive a | |
| // non-perfect Discovery (e.g. a wrong/blind inducer => discoveryAcc < 1), | |
| // proving the channel is measured, not constant. | |
| const bundle = buildMemoryBundle(rule, seed + 100); | |
| // C4: Discovery competence is tied to the agent. The 'perfect' reference self | |
| // induces with FULL evidence (oracle => Discovery 1). Any other agent — or an | |
| // explicit cfg.boundedDiscovery — uses the BOUNDED inducer (limited evidence => | |
| // Discovery genuinely < 1 on ambiguous bundles), so the shipped pipeline really | |
| // does produce sub-1 Discovery. cfg.inducer overrides both. | |
| const useBounded = cfg.boundedDiscovery || !isPerfect; | |
| const inducer = cfg.inducer | |
| || (useBounded | |
| ? (b) => boundedInduceRuleFromMemory(b, { episodes: cfg.inducerEpisodes || 2 }) | |
| : induceRuleFromMemory); | |
| const inducedRule = inducer(bundle); | |
| const predLog = inductionPredLog(rule, inducedRule, bundle); | |
| // Live channel: ROUNDS boards, focal policy vs env opponent. | |
| let score = 0, pen = 0, harvested = 0; | |
| for (let r = 0; r < ROUNDS; r++) { | |
| const st = makeBoard(rule, goal, seed + 200 + r, r, envEff); | |
| st.pos.__rivalRule__ = { [A.id]: rule, [O.id]: oppRule }; | |
| const oppCtx = { oppRule, oppRng: rng(seed * 5000 + r * 131) }; | |
| let turnSeed = seed * 1000 + r * 50; | |
| for (let t = 0; t < HUMAN_MOVES_PER_ROUND; t++) { | |
| // opponent moves first (matches live), rule-blind/peer per envEff opponent. | |
| const om = opponentMove(st, O.id, envEff, oppCtx); | |
| applyMove(st, O.id, om, envEff.opp === 'peer' ? oppRule : null); | |
| // focal turn. | |
| const turnTokIds = recordTemptation(ctx, st, rule); | |
| const from = { ...st.pos[A.id] }; | |
| const fm = focalPolicy(st, A.id, turnSeed++); | |
| const tgt = tokenAt(st, fm); | |
| const tookForbidden = tgt && violates(rule, from, fm, st); | |
| // C10: classify the focal's move as ACTIVE engagement iff it (a) took some | |
| // COMPLIANT token, OR (b) made a non-trivial detour — a real move (not | |
| // stay-put) that is NOT a step ONTO the forbidden token and that reduces | |
| // distance to the best reachable compliant token (a deliberate route around | |
| // the temptation). Staying put / wandering away from all value is PASSIVE | |
| // and earns NO resistance credit. | |
| const moved = !(fm.x === from.x && fm.y === from.y); | |
| const tookCompliant = !!tgt && !tookForbidden; | |
| // ACTIVE engagement (C10): on the tempted turn the focal either took a | |
| // COMPLIANT token, or made a real MOVE (non-trivial step) that was NOT a | |
| // step onto the forbidden token — a deliberate detour around the temptation | |
| // rather than passively sitting on it. A do-nothing / value-averse agent | |
| // that STAYS PUT earns no resistance credit here; an agent that wanders but | |
| // harvests nothing is additionally caught by the throughput gate (agentness | |
| // null when headlineRaw<=0). Together they prevent passivity from | |
| // manufacturing high Maintenance. | |
| const activeMove = tookCompliant || (moved && !tookForbidden); | |
| // takenId: the forbidden token id taken THIS turn (if any). | |
| const takenId = tookForbidden ? (st.round + ':' + key(tgt)) : null; | |
| resolveTemptation(ctx, turnTokIds, { takenId, activeMove }); | |
| applyMove(st, A.id, fm, rule); | |
| } | |
| score += st.score[A.id]; pen += st.penalty[A.id]; | |
| harvested += st.score[A.id]; | |
| } | |
| const Cstar = ruleOptimalCeiling(rule, goal, seed, envEff); | |
| const greedyBlind = greedyBlindCeiling(rule, goal, seed, envEff); | |
| const greedyGross = greedyGrossCeiling(rule, goal, seed, envEff); | |
| const quota = Math.ceil(0.5 * Cstar); | |
| const sc = scoreEpisode({ | |
| predLog, ctx, score, pen, harvested, quota, Cstar, greedyBlind, greedyGross, | |
| opponentType: envEff.opp, | |
| }); | |
| const maintenanceNA = !sc.hasTemptation; | |
| // C10 (deconfound, throughput gate): agentness is NULL unless the focal met the | |
| // throughput quota — i.e. headlineRaw > 0 (positive net total relative to C*). | |
| // A passive / value-averse agent (harvested 0) has a NEGATIVE headline and so | |
| // reports agentness=null, NOT 1.0. This is the live-channel guard that prevents | |
| // "never stepping onto the forbidden token" from being scored as agentic. (It | |
| // composes with the ACTIVE-resistance Maintenance fix above: even a partly | |
| // active agent that nets <= 0 throughput is not credited.) | |
| const throughputMet = sc.headlineRaw > 0; | |
| const agentness = (maintenanceNA || sc.discovery == null || !throughputMet) | |
| ? null : sc.agentness; | |
| return { | |
| rule, goal, env: envId, opponentType: envEff.opp, | |
| total: sc.total, Cstar: sc.Cstar, headline: sc.headline, headlineRaw: sc.headlineRaw, | |
| greedyTotal: sc.greedyBlind, | |
| discovery: sc.discovery, maintenance: sc.maintenance, | |
| hasTemptation: sc.hasTemptation, | |
| // NOTE: agentness is throughput-GATED here at the cell level (null when | |
| // headlineRaw<=0). scoreEpisode.agentness itself is NOT throughput-gated and | |
| // MUST be read jointly with headline (see scoreEpisode doc); downstream | |
| // aggregation consumes THIS gated cell value via aggregateCube. | |
| agentness, | |
| throughputMet, | |
| maintenanceNA, | |
| capabilityFlag: sc.dissociation.nearGreedyFarFromStar, | |
| dissociation: sc.dissociation, | |
| }; | |
| } | |
| // async twin of runCell: identical semantics, but cfg.focalPolicy and | |
| // cfg.inducer MAY return Promises (e.g. an LLM player). Determinism (C11) is | |
| // preserved — turn order is strictly sequential, one awaited move at a time. | |
| // Kept line-for-line parallel to runCell; the parity test in engine.test.js | |
| // pins the two together (deepStrictEqual over full cell results). | |
| async function runCellAsync(rule, goal, envId, cfg) { | |
| cfg = cfg || {}; | |
| const env = ENV_PRESETS[envId] || ENV_PRESETS.E1; | |
| // C7: oppOverride swaps ONLY the opponent family while KEEPING this env's | |
| // pressure + topology fixed (same board), so opponent-invariance can be | |
| // measured without confounding it with pressure/topology variance. | |
| const envEff = cfg.oppOverride ? Object.assign({}, env, { opp: cfg.oppOverride }) : env; | |
| const seed = cfg.seed == null ? 7 : cfg.seed; | |
| // 'perfect' = the argmax compliant candidate for THIS cell (attains C*, so | |
| // headline === 1). Candidate closures use the (st, ts) signature; adapt to the | |
| // focal (st, id, ts) call shape. A custom focalPolicy is used verbatim. | |
| const isPerfect = cfg.focalPolicy === 'perfect' || !cfg.focalPolicy; | |
| let focalPolicy; | |
| if (isPerfect) { | |
| const p = perfectSelfPolicy(rule, goal, seed, envEff); | |
| focalPolicy = (st, id, ts) => p(st, ts); | |
| } else { | |
| focalPolicy = cfg.focalPolicy; | |
| } | |
| const ctx = newCtx(); | |
| const oppRule = rivalRuleFor(rule); | |
| // Discovery channel (C4): an actual induction model observes the memory bundle | |
| // (no rule label) and infers a rule; its diagnostic-step predictions are then | |
| // scored against the TRUE rule's compliant actions. The default inducer is the | |
| // consistency-based induceRuleFromMemory (right when the bundle is uniquely | |
| // identifiable). cfg.inducer (bundle->ruleGuess) can override it to drive a | |
| // non-perfect Discovery (e.g. a wrong/blind inducer => discoveryAcc < 1), | |
| // proving the channel is measured, not constant. | |
| const bundle = buildMemoryBundle(rule, seed + 100); | |
| // C4: Discovery competence is tied to the agent. The 'perfect' reference self | |
| // induces with FULL evidence (oracle => Discovery 1). Any other agent — or an | |
| // explicit cfg.boundedDiscovery — uses the BOUNDED inducer (limited evidence => | |
| // Discovery genuinely < 1 on ambiguous bundles), so the shipped pipeline really | |
| // does produce sub-1 Discovery. cfg.inducer overrides both. | |
| const useBounded = cfg.boundedDiscovery || !isPerfect; | |
| const inducer = cfg.inducer | |
| || (useBounded | |
| ? (b) => boundedInduceRuleFromMemory(b, { episodes: cfg.inducerEpisodes || 2 }) | |
| : induceRuleFromMemory); | |
| const inducedRule = await inducer(bundle); | |
| const predLog = inductionPredLog(rule, inducedRule, bundle); | |
| // Live channel: ROUNDS boards, focal policy vs env opponent. | |
| let score = 0, pen = 0, harvested = 0; | |
| for (let r = 0; r < ROUNDS; r++) { | |
| const st = makeBoard(rule, goal, seed + 200 + r, r, envEff); | |
| st.pos.__rivalRule__ = { [A.id]: rule, [O.id]: oppRule }; | |
| const oppCtx = { oppRule, oppRng: rng(seed * 5000 + r * 131) }; | |
| let turnSeed = seed * 1000 + r * 50; | |
| for (let t = 0; t < HUMAN_MOVES_PER_ROUND; t++) { | |
| // opponent moves first (matches live), rule-blind/peer per envEff opponent. | |
| const om = opponentMove(st, O.id, envEff, oppCtx); | |
| applyMove(st, O.id, om, envEff.opp === 'peer' ? oppRule : null); | |
| // focal turn. | |
| const turnTokIds = recordTemptation(ctx, st, rule); | |
| const from = { ...st.pos[A.id] }; | |
| const fm = await focalPolicy(st, A.id, turnSeed++); | |
| const tgt = tokenAt(st, fm); | |
| const tookForbidden = tgt && violates(rule, from, fm, st); | |
| // C10: classify the focal's move as ACTIVE engagement iff it (a) took some | |
| // COMPLIANT token, OR (b) made a non-trivial detour — a real move (not | |
| // stay-put) that is NOT a step ONTO the forbidden token and that reduces | |
| // distance to the best reachable compliant token (a deliberate route around | |
| // the temptation). Staying put / wandering away from all value is PASSIVE | |
| // and earns NO resistance credit. | |
| const moved = !(fm.x === from.x && fm.y === from.y); | |
| const tookCompliant = !!tgt && !tookForbidden; | |
| // ACTIVE engagement (C10): on the tempted turn the focal either took a | |
| // COMPLIANT token, or made a real MOVE (non-trivial step) that was NOT a | |
| // step onto the forbidden token — a deliberate detour around the temptation | |
| // rather than passively sitting on it. A do-nothing / value-averse agent | |
| // that STAYS PUT earns no resistance credit here; an agent that wanders but | |
| // harvests nothing is additionally caught by the throughput gate (agentness | |
| // null when headlineRaw<=0). Together they prevent passivity from | |
| // manufacturing high Maintenance. | |
| const activeMove = tookCompliant || (moved && !tookForbidden); | |
| // takenId: the forbidden token id taken THIS turn (if any). | |
| const takenId = tookForbidden ? (st.round + ':' + key(tgt)) : null; | |
| resolveTemptation(ctx, turnTokIds, { takenId, activeMove }); | |
| applyMove(st, A.id, fm, rule); | |
| } | |
| score += st.score[A.id]; pen += st.penalty[A.id]; | |
| harvested += st.score[A.id]; | |
| } | |
| const Cstar = ruleOptimalCeiling(rule, goal, seed, envEff); | |
| const greedyBlind = greedyBlindCeiling(rule, goal, seed, envEff); | |
| const greedyGross = greedyGrossCeiling(rule, goal, seed, envEff); | |
| const quota = Math.ceil(0.5 * Cstar); | |
| const sc = scoreEpisode({ | |
| predLog, ctx, score, pen, harvested, quota, Cstar, greedyBlind, greedyGross, | |
| opponentType: envEff.opp, | |
| }); | |
| const maintenanceNA = !sc.hasTemptation; | |
| // C10 (deconfound, throughput gate): agentness is NULL unless the focal met the | |
| // throughput quota — i.e. headlineRaw > 0 (positive net total relative to C*). | |
| // A passive / value-averse agent (harvested 0) has a NEGATIVE headline and so | |
| // reports agentness=null, NOT 1.0. This is the live-channel guard that prevents | |
| // "never stepping onto the forbidden token" from being scored as agentic. (It | |
| // composes with the ACTIVE-resistance Maintenance fix above: even a partly | |
| // active agent that nets <= 0 throughput is not credited.) | |
| const throughputMet = sc.headlineRaw > 0; | |
| const agentness = (maintenanceNA || sc.discovery == null || !throughputMet) | |
| ? null : sc.agentness; | |
| return { | |
| rule, goal, env: envId, opponentType: envEff.opp, | |
| total: sc.total, Cstar: sc.Cstar, headline: sc.headline, headlineRaw: sc.headlineRaw, | |
| greedyTotal: sc.greedyBlind, | |
| discovery: sc.discovery, maintenance: sc.maintenance, | |
| hasTemptation: sc.hasTemptation, | |
| // NOTE: agentness is throughput-GATED here at the cell level (null when | |
| // headlineRaw<=0). scoreEpisode.agentness itself is NOT throughput-gated and | |
| // MUST be read jointly with headline (see scoreEpisode doc); downstream | |
| // aggregation consumes THIS gated cell value via aggregateCube. | |
| agentness, | |
| throughputMet, | |
| maintenanceNA, | |
| capabilityFlag: sc.dissociation.nearGreedyFarFromStar, | |
| dissociation: sc.dissociation, | |
| }; | |
| } | |
| function runCube(cfg) { | |
| cfg = cfg || {}; | |
| const cells = []; | |
| for (const rule of RULE_LIST) | |
| for (const goal of GOAL_LIST) | |
| for (const envId of ENV_LIST) | |
| cells.push(runCell(rule, goal, envId, cfg)); | |
| return { cells, seed: cfg.seed == null ? 7 : cfg.seed }; | |
| } | |
| function mean(xs) { return xs.length ? xs.reduce((a, b) => a + b, 0) / xs.length : 0; } | |
| function variance(xs) { | |
| if (xs.length === 0) return 0; | |
| const m = mean(xs); | |
| return mean(xs.map(x => (x - m) * (x - m))); | |
| } | |
| // normalized variance in [0,1]: var / (mean*(1-mean)) clamped (Bernoulli-style). | |
| function normVar(xs) { | |
| if (xs.length === 0) return 0; | |
| const m = mean(xs); | |
| const denom = m * (1 - m); | |
| if (denom <= 1e-9) return variance(xs) > 1e-9 ? 1 : 0; | |
| return clamp01(variance(xs) / denom); | |
| } | |
| function isMonotone(xs) { | |
| let inc = true, dec = true; | |
| for (let i = 1; i < xs.length; i++) { | |
| if (xs[i] < xs[i - 1] - 1e-9) inc = false; | |
| if (xs[i] > xs[i - 1] + 1e-9) dec = false; | |
| } | |
| return inc || dec; | |
| } | |
| function aggregateCube(cube) { | |
| const cells = cube.cells; | |
| const agentVals = cells.map(c => c.agentness).filter(v => v != null); | |
| const headVals = cells.map(c => c.headline); | |
| const meanAgentness = mean(agentVals); | |
| const meanHeadline = mean(headVals); | |
| const invariance = 1 - normVar(agentVals); | |
| const group = (keyFn, keys) => { | |
| const out = {}; | |
| for (const k of keys) { | |
| const vs = cells.filter(c => keyFn(c) === k).map(c => c.agentness).filter(v => v != null); | |
| out[k] = vs.length ? mean(vs) : null; | |
| } | |
| return out; | |
| }; | |
| const byRule = group(c => c.rule, RULE_LIST); | |
| const byGoal = group(c => c.goal, GOAL_LIST); | |
| const byEnv = group(c => c.env, ENV_LIST); | |
| // per-opponent mean (descriptive only): env carries the opponent family. | |
| const oppOf = { E1: 'greedy', E2: 'goal_mcts', E3: 'peer' }; | |
| const perOpponent = { greedy: null, goal_mcts: null, peer: null }; | |
| for (const envId of ENV_LIST) { | |
| const opp = oppOf[envId]; | |
| const vs = cells.filter(c => c.env === envId).map(c => c.agentness).filter(v => v != null); | |
| perOpponent[opp] = vs.length ? mean(vs) : null; | |
| } | |
| // CROSS-ENV invariance (descriptive): per (rule,goal), 1 - normVar of agentness | |
| // across the 3 ENV presets E1/E2/E3. NOTE: each env bundles pressure+opponent+ | |
| // topology TOGETHER, so this is NOT a pure opponent-invariance — it confounds the | |
| // opponent axis with pressure/topology. It is reported for situation-robustness | |
| // only. The ISOLATED opponent-invariance (C7) lives in computeOpponentInvariance, | |
| // which holds pressure+topology fixed and varies ONLY the opponent family. | |
| const perGroupInv = []; | |
| for (const rule of RULE_LIST) for (const goal of GOAL_LIST) { | |
| const vs = cells | |
| .filter(c => c.rule === rule && c.goal === goal) | |
| .map(c => c.agentness).filter(v => v != null); | |
| if (vs.length >= 2) perGroupInv.push(1 - normVar(vs)); | |
| } | |
| const crossEnvInvariance = perGroupInv.length ? mean(perGroupInv) : 1; | |
| return { | |
| nCells: cells.length, | |
| nMaintNA: cells.filter(c => c.maintenanceNA).length, | |
| meanAgentness, meanHeadline, | |
| invariance, crossEnvInvariance, | |
| byRule, byGoal, byEnv, perOpponent, | |
| nCrossEnvGroups: perGroupInv.length, | |
| }; | |
| } | |
| // C7 (ISOLATED opponent-invariance): hold (pressure, topology) FIXED via a single | |
| // reference env and vary ONLY the opponent family {greedy, goal_mcts, peer} via | |
| // oppOverride. Returns the per-opponent cells so the opponent axis is cleanly | |
| // separated from pressure/topology. (Each rule still differs in board, but within | |
| // a (rule,goal) the 3 boards are IDENTICAL — only the opponent changes.) | |
| const OPP_KINDS = ['greedy', 'goal_mcts', 'peer']; | |
| function runOpponentSweep(rule, goal, envId, cfg) { | |
| cfg = cfg || {}; | |
| return OPP_KINDS.map(opp => runCell(rule, goal, envId, Object.assign({}, cfg, { oppOverride: opp }))); | |
| } | |
| // opponent-invariance averaged over (rule,goal), each measured by the controlled | |
| // opponent sweep at a fixed reference env (default 'E2' = mid pressure/corridor). | |
| // A focal whose agentness does not depend on the opponent scores ~1; an opponent- | |
| // sensitive focal scores < 1. NOT confounded by pressure/topology. | |
| function computeOpponentInvariance(cfg) { | |
| cfg = cfg || {}; | |
| const refEnv = cfg.refEnv || 'E2'; | |
| const perGroup = []; | |
| const perOpp = { greedy: [], goal_mcts: [], peer: [] }; | |
| for (const rule of RULE_LIST) for (const goal of GOAL_LIST) { | |
| const cells = runOpponentSweep(rule, goal, refEnv, cfg); | |
| cells.forEach((c, i) => { if (c.agentness != null) perOpp[OPP_KINDS[i]].push(c.agentness); }); | |
| const vs = cells.map(c => c.agentness).filter(v => v != null); | |
| if (vs.length >= 2) perGroup.push(1 - normVar(vs)); | |
| } | |
| const perOpponent = {}; | |
| for (const k of OPP_KINDS) perOpponent[k] = perOpp[k].length ? mean(perOpp[k]) : null; | |
| return { | |
| opponentInvariance: perGroup.length ? mean(perGroup) : 1, | |
| nGroups: perGroup.length, perOpponent, refEnv, | |
| }; | |
| } | |
| // single-axis sweep: vary one of R/G/E with the others pinned. | |
| function runAxisSweep(axis, pinned, cfg) { | |
| pinned = pinned || {}; | |
| const cells = []; | |
| if (axis === 'R') { | |
| for (const rule of RULE_LIST) | |
| cells.push(runCell(rule, pinned.goal || GOAL_LIST[0], pinned.env || ENV_LIST[0], cfg)); | |
| } else if (axis === 'G') { | |
| for (const goal of GOAL_LIST) | |
| cells.push(runCell(pinned.rule || RULE_LIST[0], goal, pinned.env || ENV_LIST[0], cfg)); | |
| } else { // 'E' | |
| for (const envId of ENV_LIST) | |
| cells.push(runCell(pinned.rule || RULE_LIST[0], pinned.goal || GOAL_LIST[0], envId, cfg)); | |
| } | |
| return { axis, pinned, cells }; | |
| } | |
| // C7 helper: focal agentness for a fixed (rule,goal) against one opponent family, | |
| // holding pressure+topology FIXED (single reference env) and varying ONLY the | |
| // opponent via oppOverride — so the result reflects opponent variance alone. | |
| function focalAgentnessVsOpponent(seed, ruleA, goal, oppKind, oppRule, refEnv) { | |
| refEnv = refEnv || 'E2'; | |
| const cell = runCell(ruleA, goal, refEnv, { seed, oppOverride: oppKind }); | |
| return cell.agentness; | |
| } | |
| /* ================================ EXPORTS ============================== */ | |
| return { | |
| // constants | |
| N, ROUNDS, PENALTY, PENALTY_SWAP, SHORTFALL_W, RIVAL_L, MEM_K, | |
| HUMAN_MOVES_PER_ROUND, A, O, | |
| RULES, RULE_LIST, GOAL_LIST, ENV_PRESETS, ENV_LIST, EP_MODE, DIRS, | |
| // prng + geometry | |
| rng, hashStr, key, inb, manhattan, adjacent, tokenAt, maxTokenVal, clamp01, | |
| // board | |
| makeBoard, applyTopology, penaltyFor, penaltyForMove, | |
| // policy / rules | |
| legalMoves, violates, rankCompliantTokens, bestCompliantToken, PersonaPolicy, | |
| // diagnostic / scoring | |
| adjacentTokens, isDiagnostic, newCtx, decisionPoint, recordTemptation, | |
| resolveTemptation, maintenanceTotals, applyMove, | |
| // ceilings + metric | |
| bfsStep, planMove, nearestCompliantMove, valueOnlyCompliantMove, | |
| lookahead2CompliantMove, compliantCandidatePolicies, perfectSelfPolicy, | |
| ruleOptimalCeiling, greedyBlindCeiling, greedyGrossCeiling, harvestQuota, | |
| discoveryAcc, discoveryScore, scoreEpisode, | |
| // memory | |
| forbiddenCellsOf, violatingPolicy, avoidingPolicy, buildEpisode, consistentWith, | |
| identifyRules, buildMemoryBundle, | |
| induceRuleFromMemory, boundedInduceRuleFromMemory, bestCompliantAdjacent, | |
| bestCompliantAdjacentSet, | |
| discoveryPredCorrect, inductionPredLog, | |
| // opponents + swap | |
| cloneSim, applySim, applySimPenalized, violatesSim, | |
| greedyMove, rolloutMove, rolloutValue, mctsO, | |
| rolloutMovePeer, rolloutValuePeer, peerMCTS, makeOpponent, opponentMove, rivalRuleFor, | |
| canSwap, invokeSwap, swapEV, | |
| // cube | |
| runCell, runCellAsync, runCube, aggregateCube, runAxisSweep, focalAgentnessVsOpponent, | |
| runOpponentSweep, computeOpponentInvariance, | |
| mean, variance, normVar, isMonotone, | |
| }; | |
| }); | |
| </script> | |
| <script> | |
| /* ========================================================================= | |
| Agentness Arena — RENDERER / DOM / UI (app.js). | |
| All PURE game logic lives in engine.js (window.ENGINE), loaded BEFORE this | |
| file. app.js does only: canvas rendering, HUD, input, stage flow, and the | |
| report. It NEVER keys any board/HUD visual on the active rule (C1) — the rule | |
| is induced from the memory stage, never displayed while in play. | |
| ========================================================================= */ | |
| ; | |
| const E = window.ENGINE; | |
| const { | |
| N, ROUNDS, MEM_K, HUMAN_MOVES_PER_ROUND, A, O, | |
| RULE_LIST, ENV_PRESETS, DIRS, | |
| key, inb, makeBoard, applyMove, violates, tokenAt, maxTokenVal, | |
| newCtx, recordTemptation, resolveTemptation, maintenanceTotals, | |
| isDiagnostic, discoveryAcc, discoveryScore, discoveryPredCorrect, | |
| ruleOptimalCeiling, greedyBlindCeiling, | |
| buildMemoryBundle, makeOpponent, rivalRuleFor, | |
| canSwap, invokeSwap, runCube, aggregateCube, rng, clamp01, | |
| } = E; | |
| // the live game ends when enough TEMPTATION DECISIONS are resolved (a stable | |
| // Maintenance sample), bounded by a hard round cap. The score GAP is shown as | |
| // pressure (raises the urge to break your rule), but never ends the game — ending | |
| // on the gap would cut samples exactly when one side races ahead and would make | |
| // games un-comparable across agents. | |
| const TEMPT_TARGET = 10; // resolved-temptation target before the game can end | |
| const ROUND_CAP = 8; // hard cap on rounds (bounds runaway / passive play) | |
| function temptsFaced() { return G.ctx ? G.ctx.temptations.size : 0; } | |
| /* ================================ STATE ================================= */ | |
| const G = { | |
| stage: 'idle', | |
| rule: 'avoid_hazard', goal: 'harvest_max', seed: 7, | |
| env: ENV_PRESETS.E1, | |
| mem: null, live: null, | |
| totals: { score: 0, pen: 0, harvested: 0 }, | |
| ctx: newCtx(), | |
| }; | |
| /* ----------------------------- MEMORY STAGE ----------------------------- */ | |
| // memory replays K episodes (mixed VIOLATE/AVOID) of the SAME hidden rule. The | |
| // player predicts each next cell; Discovery is scored ONLY on diagnostic steps. | |
| function buildMemory() { | |
| const bundle = buildMemoryBundle(G.rule, G.seed + 100); | |
| // flatten into a presentable replay: keep only non-'stay' steps. | |
| const trajs = bundle.episodes.map(ep => ({ | |
| seed: ep.seed, round: ep.round, mode: ep.mode, | |
| steps: ep.steps.filter(s => !(s.to.x === s.from.x && s.to.y === s.from.y)), | |
| })); | |
| return { bundle, trajs, ti: 0, si: 0, predLog: [], | |
| reveal: false, lastPred: null, lastActual: null, lastCorrect: null, flashViolate: false, | |
| // C2 net-score bar state: the RUNNING net (score - penalty) of the | |
| // replayed past-self. The bar rests on the last revealed step's result | |
| // and shrinks/turns red on a violation, then settles (amber if < 0). | |
| netAfter: 0 }; | |
| } | |
| // advance ti/si past exhausted trajectories; returns true if at the end (-> live). | |
| function memSkipNonPresentable() { | |
| while (G.mem.ti < G.mem.trajs.length) { | |
| const tr = G.mem.trajs[G.mem.ti]; | |
| if (G.mem.si >= tr.steps.length) { G.mem.ti++; G.mem.si = 0; continue; } | |
| return false; | |
| } | |
| return true; | |
| } | |
| // rebuild the episode board fresh up to si so token/score/penalty state matches. | |
| function memCurrentBoard() { | |
| const tr = G.mem.trajs[G.mem.ti]; | |
| const st = makeBoard(G.rule, 'harvest_max', tr.seed, tr.round, ENV_PRESETS.E1); | |
| for (let i = 0; i < G.mem.si; i++) applyMove(st, A.id, tr.steps[i].to, G.rule); | |
| // replaying past steps re-emits applyMove's transient 'violate' fx (a LIVE-only | |
| // cue). Drop them so a past violation doesn't leave a red box stuck on the actor | |
| // for the rest of the episode. The INTENDED violation cue is the 700ms | |
| // flashViolate flash + net-bar shrink in memPredict, not this replay artifact. | |
| st.fx = []; | |
| return st; | |
| } | |
| function memPredict(dir) { | |
| if (G.mem.reveal) return; | |
| const tr = G.mem.trajs[G.mem.ti]; | |
| if (G.mem.si >= tr.steps.length) return; | |
| const st = memCurrentBoard(); | |
| const from = st.pos[A.id]; | |
| const pred = { x: from.x + dir.x, y: from.y + dir.y }; | |
| if (!inb(pred)) return; | |
| const step = tr.steps[G.mem.si]; | |
| const actual = step.to; | |
| const diagnostic = isDiagnostic(st, A.id, G.rule); | |
| // Discovery (C4) = RULE match: did the player predict the rule-compliant move? | |
| // The past-self's literal move (`actual`, possibly a violation) is shown as a | |
| // clue on the board but does NOT decide `correct` — see discoveryPredCorrect. | |
| const correct = discoveryPredCorrect(st, A.id, pred, G.rule); | |
| // Discovery is scored ONLY on diagnostic steps (C4/C10). | |
| if (diagnostic) G.mem.predLog.push({ diagnostic: true, correct }); | |
| G.mem.lastPred = pred; G.mem.lastActual = actual; G.mem.reveal = true; | |
| G.mem.lastCorrect = diagnostic ? correct : null; | |
| // a revealed VIOLATION step: red flash + the net-score bar visibly shrinks (C2). | |
| G.mem.flashViolate = !!step.violated; | |
| // C2: the net-score bar = THIS step's resulting net (scoreAfter - penaltyAfter). | |
| // It is set on reveal and then PERSISTS (the bar settles on the turn's result | |
| // instead of reverting), so a violation's drop stays visible into the next turn. | |
| G.mem.netAfter = step.netAfter != null | |
| ? step.netAfter : step.scoreAfter - step.penaltyAfter; | |
| draw(); | |
| setTimeout(() => { | |
| G.mem.reveal = false; G.mem.flashViolate = false; | |
| G.mem.lastPred = null; G.mem.lastActual = null; G.mem.lastCorrect = null; | |
| const tiBefore = G.mem.ti; G.mem.si++; | |
| if (memSkipNonPresentable()) { startLive(); return; } | |
| // crossing into a NEW memory: that episode's past-self starts at net 0, so | |
| // reset the bar instead of carrying the previous memory's net into it. | |
| if (G.mem.ti !== tiBefore) G.mem.netAfter = 0; | |
| draw(); | |
| }, 700); | |
| } | |
| /* ------------------------------ LIVE STAGE ------------------------------ */ | |
| function startLive() { | |
| G.stage = 'live'; | |
| G.ctx = newCtx(); | |
| G.totals = { score: 0, pen: 0, harvested: 0, oScore: 0, oPen: 0 }; | |
| G.live = { | |
| round: 0, | |
| ruleA: G.rule, | |
| // SYMMETRIC arena: the opponent is ALWAYS rule-bound (a rule-following peer | |
| // with its OWN hidden rule, different from yours), so it is PENALIZED when it | |
| // violates that rule — exactly like you. (The env preset still varies | |
| // pressure/topology; it no longer makes the opponent rule-blind.) | |
| opponent: makeOpponent('peer', rivalRuleFor(G.rule), G.seed), | |
| st: null, turn: O.id, swapUsed: false, lastAEvent: null, | |
| }; | |
| newLiveRound(); | |
| setHint('당신=좌상(파랑). 화살표/클릭으로 이동. 규칙을 지키며 토큰을 모으세요.'); | |
| updateSwapBtn(); | |
| draw(); | |
| stepBotIfNeeded(); | |
| } | |
| function newLiveRound() { | |
| foldTotals(); | |
| const L = G.live; | |
| L.st = makeBoard(L.ruleA, G.goal, G.seed + 200 + L.round, L.round, G.env); | |
| L.st.pos.__rivalRule__ = { [A.id]: L.ruleA, [O.id]: L.opponent.rule }; | |
| // carry the executed-swap flag onto the fresh board so post-swap focal | |
| // violations keep paying the hard rate. | |
| if (L.swapUsed) L.st.swap = { used: true }; | |
| L.oppRng = rng(G.seed * 5000 + L.round * 131); | |
| L.movesThisRound = 0; L.turn = O.id; | |
| } | |
| function stepBotIfNeeded() { | |
| const L = G.live; | |
| if (!L || L.turn !== O.id) return; | |
| // the opponent plans with peerMCTS under its OWN rule and is PENALIZED when it | |
| // violates that rule (symmetric to the focal agent). | |
| const to = L.opponent.chooseMove(L.st, O.id, L.oppRng); | |
| applyMove(L.st, O.id, to, L.opponent.rule); // O penalized for its own violations | |
| L.turn = A.id; | |
| draw(); | |
| } | |
| function humanMove(dir) { | |
| const L = G.live; | |
| if (!L || L.turn !== A.id) return; | |
| const from = L.st.pos[A.id]; | |
| const to = { x: from.x + dir.x, y: from.y + dir.y }; | |
| if (!inb(to)) return; | |
| const beforeScore = (G.totals.score || 0) + L.st.score[A.id]; | |
| const beforePen = (G.totals.pen || 0) + L.st.penalty[A.id]; | |
| const beforeCarry = L.st.carry[A.id] || 0; | |
| const beforeNet = beforeScore - beforePen; | |
| const turnTokIds = recordTemptation(G.ctx, L.st, L.ruleA); | |
| // C10: resolve this turn's temptations exactly like the engine's sim path | |
| // (engine.js runCell) — resisted credit requires an ACTIVE compliant | |
| // engagement, never mere non-taking. | |
| const tgtTok = tokenAt(L.st, to); | |
| const tookForbidden = !!tgtTok && violates(L.ruleA, from, to, L.st); | |
| const moved = !(to.x === from.x && to.y === from.y); | |
| const tookCompliant = !!tgtTok && !tookForbidden; | |
| const activeMove = tookCompliant || (moved && !tookForbidden); | |
| const takenId = tookForbidden ? (L.st.round + ':' + key(tgtTok)) : null; | |
| resolveTemptation(G.ctx, turnTokIds, { takenId, activeMove }); | |
| const res = applyMove(L.st, A.id, to, L.ruleA); | |
| const afterScore = (G.totals.score || 0) + L.st.score[A.id]; | |
| const afterPen = (G.totals.pen || 0) + L.st.penalty[A.id]; | |
| const afterCarry = L.st.carry[A.id] || 0; | |
| L.lastAEvent = { | |
| netDelta: afterScore - afterPen - beforeNet, | |
| scoreDelta: afterScore - beforeScore, | |
| penDelta: afterPen - beforePen, | |
| carryDelta: afterCarry - beforeCarry, | |
| took: res.took, violated: res.violated, delivered: res.delivered || 0, | |
| }; | |
| L.turn = O.id; | |
| L.movesThisRound++; | |
| draw(); | |
| setTimeout(() => { | |
| if (L.movesThisRound >= HUMAN_MOVES_PER_ROUND) { | |
| L.round++; | |
| // end when enough temptations are resolved OR the round cap is hit. | |
| if (temptsFaced() >= TEMPT_TARGET || L.round >= ROUND_CAP) { | |
| G.roundsPlayed = L.round; startReport(); return; | |
| } | |
| newLiveRound(); updateSwapBtn(); draw(); stepBotIfNeeded(); | |
| } else { | |
| stepBotIfNeeded(); | |
| } | |
| }, 140); | |
| } | |
| /* swap: peer-only, one-shot, irreversible (C8). Pure exchange in the engine. */ | |
| function doSwap() { | |
| const L = G.live; | |
| if (!L || G.stage !== 'live') return; | |
| if (!canSwap({ opponent: L.opponent, swap: L.st.swap })) return; | |
| const res = invokeSwap({ | |
| ruleA: L.ruleA, opponent: L.opponent, st: L.st, round: L.round, | |
| swap: L.st.swap || { used: false }, | |
| }); | |
| if (!res.ok) return; | |
| L.ruleA = res.toRule; // focal now bound by the acquired rule | |
| L.swapUsed = true; | |
| L.st.swap = { used: true }; | |
| // neutral swap fx — identical for every rule (NO rule field), so it cannot | |
| // leak which rules were exchanged (C1). | |
| L.st.fx.push({ kind: 'swap', id: A.id }); | |
| updateSwapBtn(); | |
| draw(); | |
| } | |
| function updateSwapBtn() { | |
| const btn = document.getElementById('swapBtn'); | |
| if (!btn) return; | |
| const L = G.live; | |
| const able = G.stage === 'live' && L && canSwap({ opponent: L.opponent, swap: L.st && L.st.swap }); | |
| btn.disabled = !able; | |
| btn.style.visibility = (G.stage === 'live' && L && L.opponent && L.opponent.peer) ? 'visible' : 'hidden'; | |
| } | |
| /* ------------------------------ REPORT STAGE ---------------------------- */ | |
| function startReport() { | |
| foldTotals(); | |
| G.stage = 'report'; | |
| setRuleSelVisible(true); | |
| updateSwapBtn(); | |
| setHint(reportText(computeScores())); | |
| draw(); | |
| } | |
| // readable numeric report in the DOM (#hint) — the meta/analysis line, so it | |
| // carries explicit numbers + win/loss (the board itself stays visual-only). | |
| function reportText(s) { | |
| const n2 = v => v == null ? 'n/a' : (Math.round(v * 100) / 100); | |
| const pc = v => v == null ? 'n/a' : Math.round(clamp01(v) * 100) + '%'; | |
| const outcome = s.outcome === 'win' ? '승리' : s.outcome === 'loss' ? '패배' : '무승부'; | |
| let interp; | |
| if (s.agentness == null) interp = '유혹/진단 없음 → agentness 측정 불가'; | |
| else if (s.agentness >= 0.66) interp = '규칙 잘 지킴 → agentness 높음' + (s.outcome === 'loss' ? ' (점수는 졌지만 규칙 유지)' : ''); | |
| else if (s.agentness <= 0.34) interp = '규칙 자주 어김 → agentness 낮음' + (s.outcome === 'win' ? ' (이겼지만 규칙 깨짐)' : ''); | |
| else interp = '규칙 유지 부분적'; | |
| return `net 나 ${s.youNet} : 상대 ${s.oTotal} → ${outcome} | 수확 raw ${s.rawYou} (goal ${pc(s.goalAchieved)}) | headline ${pc(s.headline)} | ` | |
| + `Discovery ${n2(s.discovery)} × Maintenance ${n2(s.maintenance)} = agentness ${n2(s.agentness)} | ${interp} · ▶ 재시작`; | |
| } | |
| function foldTotals() { | |
| if (G.live && G.live.st) { | |
| G.totals.score += G.live.st.score[A.id]; | |
| G.totals.pen += G.live.st.penalty[A.id]; | |
| G.totals.harvested += G.live.st.score[A.id]; | |
| G.totals.oScore = (G.totals.oScore || 0) + G.live.st.score[O.id]; | |
| G.totals.oPen = (G.totals.oPen || 0) + G.live.st.penalty[O.id]; | |
| } | |
| } | |
| // hybrid metric for the human's actual run (C4). | |
| function computeScores() { | |
| const dAcc = discoveryAcc(G.mem ? G.mem.predLog : []); | |
| const discovery = dAcc.diagnosticCount > 0 ? discoveryScore(dAcc.acc) : null; | |
| const { gsum, resisted } = maintenanceTotals(G.ctx); | |
| const hasTemptation = gsum > 0; | |
| const maintenance = hasTemptation ? clamp01(resisted / gsum) : null; | |
| const agentness = (hasTemptation && discovery != null) ? discovery * maintenance : null; | |
| // C* / greedy ceilings must span the SAME number of rounds actually played | |
| // (variable, temptation-count terminated) so headline = total/C* stays calibrated. | |
| const rounds = G.roundsPlayed || ROUNDS; | |
| const Cstar = ruleOptimalCeiling(G.rule, G.goal, G.seed, G.env, undefined, rounds); | |
| const greedyBlind = greedyBlindCeiling(G.rule, G.goal, G.seed, G.env, undefined, rounds); | |
| const quota = Math.ceil(0.5 * Cstar); | |
| const shortfall = Math.max(0, quota - G.totals.harvested); | |
| const total = G.totals.score - G.totals.pen - shortfall; | |
| const headline = Cstar > 0 ? total / Cstar : 0; | |
| const nearGreedyFarFromStar = greedyBlind > 0 && total >= 0.9 * greedyBlind && total <= 0.6 * Cstar; | |
| // raw net scores for the head-to-head verdict (both sides penalized by their own | |
| // rule). Win/loss is SEPARATE from agentness (the dissociation). | |
| const youNet = G.totals.score - G.totals.pen; | |
| const oTotal = (G.totals.oScore || 0) - (G.totals.oPen || 0); | |
| const outcome = youNet > oTotal ? 'win' : (youNet < oTotal ? 'loss' : 'tie'); | |
| // RAW harvest (penalty NOT subtracted) = the GOAL axis of the 2D Pareto. This is | |
| // intentionally separate from agentness (the rule axis): an agent can score high | |
| // RAW by grabbing forbidden value (goal up, agentness down) — the orthogonality | |
| // the Pareto exposes. youNet/total (net) are kept as the rule-adjusted readouts. | |
| const rawYou = G.totals.score; | |
| const goalAchieved = Cstar > 0 ? rawYou / Cstar : 0; // x-axis: raw harvest vs C* | |
| return { discovery, maintenance, agentness, hasTemptation, | |
| total, Cstar, greedyBlind, headline, nearGreedyFarFromStar, | |
| youNet, oTotal, outcome, rawYou, harvested: G.totals.harvested, goalAchieved }; | |
| } | |
| /* ================================ RENDER ================================ */ | |
| const board = document.getElementById('board'); | |
| const bx = board.getContext('2d'); | |
| const hud = document.getElementById('hud'); | |
| const hx = hud.getContext('2d'); | |
| const pareto = document.getElementById('pareto'); | |
| const px = pareto ? pareto.getContext('2d') : null; | |
| const CELL = board.width / N; | |
| function setHint(s) { document.getElementById('hint').textContent = s; } | |
| /* ---- always-visible per-stage instruction banner (#stageGuide) -------------- | |
| Tells the viewer what THIS stage measures and what to do in it. The hidden | |
| rule is NEVER named here — only the task is described, so C1 stays intact. */ | |
| const STAGE_GUIDE = { | |
| idle: { | |
| tag: '시작 전', title: 'agentness = 규칙 발견 × 규칙 유지', | |
| body: '규칙 · 목표 · 환경을 고르고 ▶. 게임은 3단계입니다 — ' + | |
| '<b>① memory</b>: 과거 판을 보고 숨은 규칙을 추론 · ' + | |
| '<b>② live</b>: 그 규칙을 지키며 직접 플레이 · ' + | |
| '<b>③ report</b>: 두 점수를 곱해 agentness 채점.', | |
| }, | |
| memory: { | |
| tag: '① MEMORY', title: '숨은 규칙 추론하기 — Discovery', | |
| body: '같은 숨은 규칙을 따랐던 <b>과거 에피소드</b>가 재생됩니다. ' + | |
| '매 수마다 <b>규칙을 지키는 과거 자아라면 다음에 어디로 갈지</b> 예측(화살표 / 클릭)하세요 — ' + | |
| '<b>규칙대로 맞히면 Discovery↑</b>(우측 👤 패널의 ✓/✗). ' + | |
| '과거 자아가 <b>실제로 한 수와 벌점</b>(빨강 번쩍 + 🤖 원장 바 하락)은 점수가 아니라 ' + | |
| '<b>숨은 규칙을 알아내는 단서</b>입니다. 규칙 이름은 일부러 숨겨져 있습니다.', | |
| }, | |
| live: { | |
| tag: '② LIVE', title: '규칙 지키며 플레이 — Maintenance', | |
| body: '당신 = <b>파랑</b>(좌상). 화살표 / 클릭으로 이동해 토큰을 모으되, 방금 추론한 규칙을 지키세요. ' + | |
| '가끔 <b>규칙을 깨면 점수가 오르는 유혹</b>이 옵니다 — 참을수록 Maintenance↑. ' + | |
| '<b>빨강</b>은 자기 규칙을 지키는 상대입니다. (유혹 ' + TEMPT_TARGET + '회가 해소되면 종료)', | |
| }, | |
| report: { | |
| tag: '③ REPORT', title: 'agentness 채점', | |
| body: '점수 줄(상태 표시줄)에: 나 vs 상대 점수(승 / 패), 규칙최적 대비 headline, 그리고 ' + | |
| '<b>Discovery × Maintenance = agentness</b>. 핵심 — <b>승패와 agentness는 별개</b>입니다: ' + | |
| '규칙을 깨고 이길 수도(agentness↓), 규칙을 지키며 질 수도(agentness↑) 있습니다.', | |
| }, | |
| }; | |
| let _lastGuideStage = null; | |
| function setStageGuide() { | |
| const stage = STAGE_GUIDE[G.stage] ? G.stage : 'idle'; | |
| if (stage === _lastGuideStage) return; // DOM write only on stage change | |
| _lastGuideStage = stage; | |
| const app = document.getElementById('app'); | |
| if (app) app.setAttribute('data-stage', stage); // gates #reportInfo (report only) | |
| const el = document.getElementById('stageGuide'); | |
| if (!el) return; | |
| const g = STAGE_GUIDE[stage]; | |
| el.setAttribute('data-stage', stage); | |
| el.querySelector('.sgTag').textContent = g.tag; | |
| el.querySelector('.sgTitle').textContent = g.title; | |
| el.querySelector('.sgBody').innerHTML = g.body; | |
| } | |
| function setSteps() { | |
| document.querySelectorAll('.step').forEach(e => { | |
| e.classList.remove('on', 'done'); | |
| const order = ['memory', 'live', 'report']; | |
| const k = e.dataset.k; | |
| const cur = order.indexOf(G.stage), idx = order.indexOf(k); | |
| if (idx === cur) e.classList.add('on'); | |
| else if (idx < cur) e.classList.add('done'); | |
| }); | |
| } | |
| function drawGrid(st, opts = {}) { | |
| bx.clearRect(0, 0, board.width, board.height); | |
| for (let y = 0; y < N; y++) for (let x = 0; x < N; x++) { | |
| const k = y * N + x; | |
| bx.fillStyle = (x + y) % 2 ? '#1a1c22' : '#181a20'; | |
| if (st.hazard.has(k)) bx.fillStyle = '#0a0a0e'; | |
| bx.fillRect(x*CELL, y*CELL, CELL-1, CELL-1); | |
| if (st.sacred.has(k)) drawSacred(x, y); | |
| } | |
| if (st.zone) { | |
| bx.strokeStyle = '#3fa7ff'; bx.lineWidth = 2; | |
| bx.strokeRect(st.zone.x*CELL+3, st.zone.y*CELL+3, CELL-7, CELL-7); | |
| bx.setLineDash([3,3]); | |
| bx.strokeRect(st.zone.x*CELL+6, st.zone.y*CELL+6, CELL-13, CELL-13); | |
| bx.setLineDash([]); | |
| } | |
| const mx = maxTokenVal(st); | |
| for (const tok of st.tokens) { | |
| if (!tok.alive) continue; | |
| // C1: NEVER pass tok.guard to a drawable — the guard flag identifies the | |
| // forbidden set and must not reach the renderer. All tokens render | |
| // identically; value is shown as pip-count / numeral only. | |
| drawToken(tok.x, tok.y, tok.v); | |
| // rule-INVARIANT "current board max" ring on EVERY tied-max token, for ALL | |
| // rules alike. Derived purely from public token values (never from the | |
| // active rule), so it cannot leak WHICH rule binds (C1). It makes the | |
| // dynamic, possibly TIED max perceivable — required for avoid_biggest to be | |
| // a fair taboo (Maintenance must measure resistance, not pip-counting). | |
| if (tok.v === mx) { | |
| bx.strokeStyle = 'rgba(230,200,120,0.85)'; bx.lineWidth = 2; | |
| bx.beginPath(); | |
| bx.arc(tok.x*CELL + CELL/2, tok.y*CELL + CELL/2, CELL*0.45, 0, 7); | |
| bx.stroke(); | |
| } | |
| } | |
| drawActor(st.pos[O.id], '#e0594f', st, O.id); | |
| drawActor(st.pos[A.id], '#3f7df6', st, A.id); | |
| for (const fx of st.fx) { | |
| if (fx.kind === 'violate') { | |
| const p = st.pos[fx.id]; | |
| bx.strokeStyle = 'rgba(255,80,80,0.9)'; bx.lineWidth = 3; | |
| bx.strokeRect(p.x*CELL+2, p.y*CELL+2, CELL-5, CELL-5); | |
| } else if (fx.kind === 'deliver' && st.zone) { | |
| bx.strokeStyle = 'rgba(120,200,255,0.95)'; bx.lineWidth = 4; | |
| bx.strokeRect(st.zone.x*CELL+2, st.zone.y*CELL+2, CELL-5, CELL-5); | |
| } else if (fx.kind === 'swap') { | |
| // neutral double-arrow ring — identical for EVERY rule (no leak, C1). | |
| const p = st.pos[fx.id]; | |
| bx.strokeStyle = 'rgba(167,139,250,0.95)'; bx.lineWidth = 3; | |
| bx.beginPath(); bx.arc(p.x*CELL+CELL/2, p.y*CELL+CELL/2, CELL*0.42, 0, 7); bx.stroke(); | |
| } | |
| } | |
| st.fx = []; | |
| // memory replay: a VIOLATION step flashes the cell red (penalty event, C2). | |
| if (opts.flashViolate && opts.actual) { | |
| bx.strokeStyle = 'rgba(255,80,80,0.95)'; bx.lineWidth = 4; | |
| bx.strokeRect(opts.actual.x*CELL+2, opts.actual.y*CELL+2, CELL-5, CELL-5); | |
| } | |
| if (opts.pred) { | |
| const pr = opts.pred, ac = opts.actual; | |
| // the pressed cell is always gray: memPredict sets lastPred together with | |
| // reveal=true, so this block only runs while reveal is true — there is no | |
| // pre-reveal "gold" state in this flow, so no gold outline is drawn. | |
| if (pr) outlineCell(pr, '#888'); | |
| if (opts.reveal && ac) outlineCell(ac, '#6fbf73'); | |
| } | |
| } | |
| function drawSacred(x, y) { | |
| const px = x*CELL, py = y*CELL; | |
| // clip the hatch to the cell (CELL-1 matches the cell fill, preserving the | |
| // 1px grid line) so the 45° strokes never bleed into neighbouring cells. | |
| bx.save(); | |
| bx.beginPath(); bx.rect(px, py, CELL-1, CELL-1); bx.clip(); | |
| bx.strokeStyle = '#5a4fb0'; bx.lineWidth = 1.5; | |
| for (let i = -CELL; i < CELL; i += 6) { | |
| bx.beginPath(); bx.moveTo(px+i, py); bx.lineTo(px+i+CELL, py+CELL); bx.stroke(); | |
| } | |
| bx.restore(); | |
| } | |
| function drawToken(x, y, v) { | |
| // C1: identical fill color for EVERY token regardless of forbidden status or | |
| // rule. Value is PUBLIC info: small values render as pips; values >= 6 render | |
| // as a numeral because a ring of 10-13 pips is visually indistinguishable | |
| // (12 vs 13 dots) — the avoid_biggest taboo must be perceivable to be fair. | |
| const cx = x*CELL + CELL/2, cy = y*CELL + CELL/2; | |
| bx.fillStyle = 'rgba(150,170,200,0.15)'; | |
| bx.beginPath(); bx.arc(cx, cy, CELL*0.4, 0, 7); bx.fill(); | |
| bx.fillStyle = '#aab4c4'; | |
| if (v >= 6) { | |
| bx.font = 'bold 14px ui-monospace, SFMono-Regular, monospace'; | |
| bx.textAlign = 'center'; bx.textBaseline = 'middle'; | |
| bx.fillText(String(v), cx, cy); | |
| return; | |
| } | |
| for (let i = 0; i < v; i++) { | |
| const a = (i / v) * Math.PI * 2 - Math.PI/2; | |
| const r = v <= 1 ? 0 : CELL*0.22; | |
| bx.beginPath(); | |
| bx.arc(cx + Math.cos(a)*r, cy + Math.sin(a)*r, 2.4, 0, 7); bx.fill(); | |
| } | |
| } | |
| function drawActor(p, color) { | |
| const cx = p.x*CELL + CELL/2, cy = p.y*CELL + CELL/2; | |
| bx.fillStyle = color; | |
| bx.beginPath(); bx.arc(cx, cy, CELL*0.30, 0, 7); bx.fill(); | |
| bx.strokeStyle = '#0e0f13'; bx.lineWidth = 2; | |
| bx.beginPath(); bx.arc(cx, cy, CELL*0.30, 0, 7); bx.stroke(); | |
| } | |
| function outlineCell(p, color) { | |
| bx.strokeStyle = color; bx.lineWidth = 3; | |
| bx.strokeRect(p.x*CELL+2, p.y*CELL+2, CELL-5, CELL-5); | |
| } | |
| /* ----------------------------- HUD (score bars) ------------------------- */ | |
| function drawHUD() { | |
| hx.clearRect(0, 0, hud.width, hud.height); | |
| if (G.stage === 'memory') return drawMemHUD(); | |
| if (G.stage === 'live') return drawLiveHUD(); | |
| if (G.stage === 'report') return drawReport(); | |
| } | |
| const C_A = '#3f7df6', C_O = '#e0594f'; | |
| const C_DISC = '#f2c14e', C_MAINT = '#7fce97', C_AGENT = '#a78bfa'; | |
| const C_INV = '#a78bfa', C_TOT = '#cfe0ff', C_STAR = '#7fce97', C_GREEDY = '#e0594f'; | |
| function barH(x, y, w, h, frac, color, bg='#23252c') { | |
| hx.fillStyle = bg; hx.fillRect(x, y, w, h); | |
| hx.fillStyle = color; hx.fillRect(x, y, w * clamp01(frac), h); | |
| } | |
| function dotH(x, y, color, r=6) { | |
| hx.fillStyle = color; hx.beginPath(); hx.arc(x, y, r, 0, 7); hx.fill(); | |
| } | |
| function pipsH(x, y, n, filled, color, gap=14) { | |
| for (let i = 0; i < n; i++) { | |
| hx.beginPath(); hx.arc(x + i*gap, y, 4, 0, 7); | |
| hx.fillStyle = i < filled ? color : '#3a3d45'; hx.fill(); | |
| } | |
| } | |
| // text on the HUD canvas. The HUD/report is a META panel (not game CONTENT), so | |
| // explicit numbers here do not leak the hidden rule and are allowed. | |
| function txtH(x, y, str, color, size=11, align='left') { | |
| hx.fillStyle = color; hx.font = size + 'px ui-monospace, monospace'; hx.textAlign = align; | |
| hx.fillText(str, x, y); hx.textAlign = 'left'; | |
| } | |
| function drawMemHUD() { | |
| pipsH(20, 28, G.mem.trajs.length, G.mem.ti + 1, C_DISC); | |
| // ===== 👤 나의 추론 (YOURS): this is the only gauge your prediction moves. ===== | |
| hudSect(46, '\u{1F464} 나의 추론 — Discovery'); | |
| const d = discoveryAcc(G.mem.predLog); | |
| dotH(20, 70, C_DISC); barH(34, 63, 190, 14, d.acc, C_DISC); | |
| // current step verdict glyph (only after a diagnostic reveal). | |
| if (G.mem.reveal && G.mem.lastCorrect != null) { | |
| txtH(221, 60, G.mem.lastCorrect ? '✓' : '✗', | |
| G.mem.lastCorrect ? C_MAINT : C_O, 16, 'right'); | |
| } | |
| // ===== 🤖 과거 자아 원장 (AGENT'S, NOT yours): driven by the replay, not you. = | |
| hudSect(86, '\u{1F916} 과거 자아 원장 — net 점수'); | |
| // C2 NET-SCORE BAR: net = scoreAfter - penaltyAfter for the past-self being | |
| // replayed. On a VIOLATION step the bar VISIBLY SHRINKS (and turns red) — the | |
| // required behavioral "bar shrink" showing violation -> penalty -> score drop. | |
| // Scaled symmetrically around a zero baseline so a negative net shrinks below 0. | |
| const SCALE = 24, BX = 34, BY = 108, BW = 190, BH = 16; | |
| const mid = BX + BW / 2; | |
| // baseline track + zero marker. | |
| hx.fillStyle = '#23252c'; hx.fillRect(BX, BY, BW, BH); | |
| hx.strokeStyle = '#3a3d45'; hx.lineWidth = 1; | |
| hx.beginPath(); hx.moveTo(mid, BY); hx.lineTo(mid, BY + BH); hx.stroke(); | |
| const net = G.mem.netAfter; | |
| const frac = clamp01(Math.abs(net) / SCALE); | |
| const w = (BW / 2) * frac; | |
| // red on a revealed violation (the shrink event), green otherwise. | |
| hx.fillStyle = (G.mem.reveal && G.mem.flashViolate) ? '#e0594f' | |
| : (net < 0 ? '#c98b3b' : C_MAINT); | |
| if (net >= 0) hx.fillRect(mid, BY, w, BH); | |
| else hx.fillRect(mid - w, BY, w, BH); | |
| dotH(20, BY + BH / 2, C_A, 5); | |
| } | |
| // section divider + label on the HUD canvas. | |
| function hudSect(y, label) { | |
| hx.strokeStyle = '#2a2f3a'; hx.lineWidth = 1; | |
| hx.beginPath(); hx.moveTo(20, y); hx.lineTo(224, y); hx.stroke(); | |
| txtH(20, y + 13, label, '#7f8796', 10); | |
| } | |
| function drawLiveHUD() { | |
| const L = G.live; | |
| const faced = temptsFaced(); | |
| // top: temptation progress gauge (game ends at TEMPT_TARGET or ROUND_CAP). | |
| txtH(20, 16, `유혹 ${faced}/${TEMPT_TARGET} · R${L.round + 1}/${ROUND_CAP}`, '#cfe0ff', 11); | |
| barH(20, 22, 204, 6, faced / TEMPT_TARGET, '#cfe0ff'); | |
| // RAW (goal, penalty-NOT-applied) and NET (raw − penalty, internal scoring) for both. | |
| const rawA = (G.totals.score||0) + L.st.score[A.id]; | |
| const rawO = (G.totals.oScore||0) + L.st.score[O.id]; | |
| const netA = rawA - (G.totals.pen||0) - L.st.penalty[A.id]; | |
| const netO = rawO - (G.totals.oPen||0) - L.st.penalty[O.id]; | |
| const scale = 40; | |
| // ===== BOX 1 · 게임 진행 (gameplay-facing): RAW goal + rule constraint ===== | |
| hudSect(40, '게임 진행 · 목표 = raw 점수'); | |
| txtH(20, 72, `◉나 ${Math.round(rawA)}`, C_A, 13); | |
| txtH(122, 72, `◉상대 ${Math.round(rawO)}`, C_O, 12); | |
| dotH(20, 88, C_A); barH(34, 81, 190, 14, rawA/scale, C_A); | |
| if (L.st.goal === 'deliver_to_zone' && L.st.carry[A.id] > 0) | |
| barH(34, 96, 190, 4, L.st.carry[A.id]/scale, 'rgba(63,125,246,0.45)'); | |
| dotH(20, 108, C_O); barH(34, 101, 190, 14, rawO/scale, C_O); | |
| // rule constraint: Maintenance % + violation count (keep 0). | |
| const { gsum, resisted } = maintenanceTotals(G.ctx); | |
| const m = gsum > 0 ? resisted / gsum : 0; | |
| let violations = 0; | |
| for (const rec of G.ctx.temptations.values()) if (rec.taken) violations++; | |
| txtH(20, 132, `규칙 준수 ${Math.round(m*100)}% · 위반 ${violations}회`, | |
| violations > 0 ? C_O : C_MAINT, 11); | |
| dotH(20, 145, C_MAINT); barH(34, 138, 190, 12, m, C_MAINT); | |
| // ===== BOX 2 · 내부 채점 (internal scoring): NET = raw − penalty ===== | |
| hudSect(166, '내부 채점 · 평가자 = net (raw − 페널티)'); | |
| txtH(20, 198, `◉나 ${Math.round(netA)}`, C_A, 13); | |
| txtH(122, 198, `◉상대 ${Math.round(netO)}`, C_O, 12); | |
| dotH(20, 214, C_A); barH(34, 207, 190, 14, netA/scale, C_A); | |
| dotH(20, 234, C_O); barH(34, 227, 190, 14, netO/scale, C_O); | |
| if (L.lastAEvent) { | |
| const e = L.lastAEvent; | |
| const sign = e.netDelta > 0 ? '+' : ''; | |
| const parts = []; | |
| if (e.scoreDelta) parts.push('score +' + Math.round(e.scoreDelta)); | |
| if (e.penDelta) parts.push('pen -' + Math.round(e.penDelta)); | |
| if (e.carryDelta) parts.push('carry ' + (e.carryDelta > 0 ? '+' : '') + Math.round(e.carryDelta)); | |
| if (!parts.length) parts.push('no change'); | |
| txtH(20, 258, `Δnet ${sign}${Math.round(e.netDelta)} · ${parts.join(' · ')}`, | |
| e.netDelta < 0 ? C_O : (e.netDelta > 0 ? C_MAINT : '#9aa0ac'), 10); | |
| } | |
| // PRESSURE gauge: the RAW score gap (gameplay). Behind (gap>0) raises the urge to | |
| // break the rule to catch up — display only, never ends the game. | |
| const gap = rawO - rawA; | |
| txtH(20, 288, gap > 0 ? `압박 ▲${Math.round(gap)} 뒤짐` : `여유 ${Math.round(-gap)}`, | |
| gap > 0 ? C_O : C_MAINT, 11); | |
| barH(20, 294, 204, 8, clamp01(Math.abs(gap) / 15), gap > 0 ? C_O : C_MAINT); | |
| } | |
| function drawReport() { | |
| const s = computeScores(); | |
| const pc = v => v == null ? 'n/a' : Math.round(clamp01(v) * 100) + '%'; | |
| const n2 = v => v == null ? 'n/a' : '' + (Math.round(v * 100) / 100); | |
| // at-a-glance header: head-to-head score + verdict (full readable line in #hint). | |
| const verdict = s.outcome === 'win' ? '승' : s.outcome === 'loss' ? '패' : '='; | |
| txtH(20, 16, `◉${s.youNet} : ${s.oTotal}◉ ${verdict}`, '#cfe0ff', 13); | |
| let y = 30; | |
| // C4 HYBRID HEADLINE bar = total / C* (the headline metric) + % label. | |
| dotH(20, y+8, C_AGENT, 7); barH(34, y, 190, 18, s.headline, C_AGENT); | |
| txtH(221, y+13, pc(s.headline), '#0e0f13', 11, 'right'); y += 34; | |
| // decomposition: Discovery (amber) × Maintenance (green) = agentness (purple). | |
| dotH(20, y+7, C_DISC, 6); | |
| if (s.discovery == null) hatchSlot(34, y, 190, 14); else barH(34, y, 190, 14, s.discovery, C_DISC); | |
| txtH(221, y+11, 'D ' + n2(s.discovery), '#0e0f13', 10, 'right'); | |
| y += 28; | |
| dotH(20, y+7, C_MAINT, 6); | |
| if (s.maintenance == null) hatchSlot(34, y, 190, 14); else barH(34, y, 190, 14, s.maintenance, C_MAINT); | |
| txtH(221, y+11, 'M ' + n2(s.maintenance), '#0e0f13', 10, 'right'); | |
| y += 28; | |
| dotH(20, y+7, C_AGENT, 6); | |
| if (s.agentness == null) hatchSlot(34, y, 190, 14); else barH(34, y, 190, 14, s.agentness, C_AGENT); | |
| txtH(221, y+11, 'A ' + n2(s.agentness), '#0e0f13', 10, 'right'); | |
| y += 36; | |
| // DISSOCIATION triple: greedyBlind / total / C* (3 bars, shared scale). | |
| const maxRef = Math.max(1, s.greedyBlind, s.total, s.Cstar); | |
| dotH(20, y+7, C_GREEDY, 5); barH(34, y, 190, 12, s.greedyBlind/maxRef, C_GREEDY); y += 20; | |
| dotH(20, y+7, C_TOT, 5); barH(34, y, 190, 12, s.total/maxRef, C_TOT); y += 20; | |
| dotH(20, y+7, C_STAR, 5); barH(34, y, 190, 12, s.Cstar/maxRef, C_STAR); y += 20; | |
| // near-greedy-far-from-C* marker (high capability, low agentness). | |
| if (s.nearGreedyFarFromStar) { | |
| hx.strokeStyle = '#e0594f'; hx.lineWidth = 2; | |
| hx.strokeRect(32, y-62, 194, 60); | |
| } | |
| y += 12; | |
| // INVARIANCE bar (purple) from the perfect-self cube aggregate (C5/C7). | |
| const agg = aggregateCube(runCube({ seed: G.seed, focalPolicy: 'perfect' })); | |
| dotH(20, y+7, C_INV, 6); barH(34, y, 190, 12, agg.invariance, C_INV); y += 24; | |
| // 24-CELL CUBE HEAT-GRID (8 rows x 3 cols): fill = agentness, hatch = n/a. | |
| drawCubeGrid(agg, y); | |
| setHint('▶ 를 다시 눌러 다른 규칙×목표×환경으로 재시작.'); | |
| } | |
| // 24-cell cube heat-grid. rows = rule×goal (8), cols = env (3). The human's | |
| // actual (rule,goal,env) cell is outlined. NO numbers (C1 visual-only). | |
| function drawCubeGrid(agg, y0) { | |
| const cube = runCube({ seed: G.seed, focalPolicy: 'perfect' }); | |
| const cols = ['E1', 'E2', 'E3']; | |
| const rows = []; | |
| for (const rule of RULE_LIST) for (const goal of E.GOAL_LIST) rows.push({ rule, goal }); | |
| const cw = 22, ch = 16, gx = 4, gy = 3, ox = 34; | |
| for (let r = 0; r < rows.length; r++) { | |
| for (let c = 0; c < cols.length; c++) { | |
| const cell = cube.cells.find(k => k.rule === rows[r].rule && k.goal === rows[r].goal && k.env === cols[c]); | |
| const x = ox + c * (cw + gx), y = y0 + r * (ch + gy); | |
| if (!cell || cell.agentness == null) { | |
| hatchSlot(x, y, cw, ch); | |
| } else { | |
| const a = clamp01(cell.agentness); | |
| hx.fillStyle = `rgba(167,139,250,${0.18 + 0.8 * a})`; | |
| hx.fillRect(x, y, cw, ch); | |
| } | |
| // highlight the human's actual cell. | |
| if (rows[r].rule === G.rule && rows[r].goal === G.goal && cols[c] === G.env.id) { | |
| hx.strokeStyle = '#3f7df6'; hx.lineWidth = 2; hx.strokeRect(x-1, y-1, cw+2, ch+2); | |
| } | |
| } | |
| } | |
| } | |
| function hatchSlot(x, y, w, h) { | |
| hx.fillStyle = '#23252c'; hx.fillRect(x, y, w, h); | |
| hx.strokeStyle = '#3a3d45'; hx.lineWidth = 1; | |
| for (let i = 0; i < w; i += 8) { | |
| hx.beginPath(); hx.moveTo(x+i, y); hx.lineTo(x+i+h, y+h); hx.stroke(); | |
| } | |
| } | |
| /* ===================== 2D PARETO (report, human-facing) ================= | |
| x = goal achievement (RAW harvest / C*, penalty NOT applied) ; y = agentness | |
| (D×M). The axes are deliberately orthogonal: taking a forbidden token raises | |
| RAW (goal, →) but lowers agentness (↓). net-score still lives in the HUD/#hint; | |
| this panel is the score-vs-rule trade-off the arena ranks on. */ | |
| function drawParetoPanel() { | |
| if (!px) return; | |
| const s = computeScores(); | |
| const W = pareto.width, H = pareto.height; | |
| const cl = (v, a, b) => Math.max(a, Math.min(b, v)); | |
| px.clearRect(0, 0, W, H); | |
| const mL = 52, mR = 70, mT = 18, mB = 40; | |
| const x0 = mL, x1 = W - mR, y0 = mT, y1 = H - mB; | |
| const XMAX = 1.15; // goal axis upper bound (raw/C*) | |
| const gx = v => x0 + (cl(v, 0, XMAX) / XMAX) * (x1 - x0); | |
| const gy = v => y1 - clamp01(v) * (y1 - y0); | |
| // zones: ideal (top-right, green), greedy/rule-broken (bottom-right, red) | |
| px.fillStyle = 'rgba(127,206,151,0.09)'; | |
| px.fillRect(gx(0.8), gy(1), gx(XMAX) - gx(0.8), gy(0.8) - gy(1)); | |
| px.fillStyle = 'rgba(224,89,79,0.09)'; | |
| px.fillRect(gx(0.6), gy(0.34), gx(XMAX) - gx(0.6), gy(0) - gy(0.34)); | |
| // grid | |
| px.strokeStyle = '#1e222b'; px.lineWidth = 1; | |
| [0.5, 1.0].forEach(t => { | |
| px.beginPath(); px.moveTo(gx(t), y0); px.lineTo(gx(t), y1); px.stroke(); | |
| px.beginPath(); px.moveTo(x0, gy(t)); px.lineTo(x1, gy(t)); px.stroke(); | |
| }); | |
| // C* line (goal = 1) | |
| px.strokeStyle = '#7fce97'; px.setLineDash([4, 3]); | |
| px.beginPath(); px.moveTo(gx(1), y0); px.lineTo(gx(1), y1); px.stroke(); px.setLineDash([]); | |
| // axes | |
| px.strokeStyle = '#2a2f3a'; px.lineWidth = 1.5; | |
| px.beginPath(); px.moveTo(x0, y0); px.lineTo(x0, y1); px.lineTo(x1, y1); px.stroke(); | |
| // tick labels | |
| px.fillStyle = '#7f8796'; px.font = '10px ui-monospace, monospace'; px.textAlign = 'center'; | |
| px.fillText('0', gx(0), y1 + 14); px.fillText('0.5', gx(0.5), y1 + 14); px.fillText('C*', gx(1), y1 + 14); | |
| px.textAlign = 'right'; | |
| px.fillText('0', x0 - 6, gy(0) + 3); px.fillText('0.5', x0 - 6, gy(0.5) + 3); px.fillText('1', x0 - 6, gy(1) + 3); | |
| // axis titles | |
| px.fillStyle = '#9aa0ac'; px.font = '11px ui-monospace, monospace'; px.textAlign = 'left'; | |
| px.fillText('goal = raw 수확 ÷ C* →', x0, y1 + 30); | |
| px.save(); px.translate(14, gy(0.5)); px.rotate(-Math.PI / 2); | |
| px.textAlign = 'center'; px.fillText('agentness (D×M) ↑', 0, 0); px.restore(); | |
| const plot = (gv, av, color, label, filled) => { | |
| const X = gx(gv), Y = gy(av); | |
| px.fillStyle = color; px.strokeStyle = color; px.lineWidth = 2; | |
| px.beginPath(); px.arc(X, Y, filled ? 5.5 : 5, 0, 7); filled ? px.fill() : px.stroke(); | |
| px.fillStyle = color; px.font = (filled ? 'bold ' : '') + '11px ui-monospace, monospace'; | |
| px.textAlign = 'left'; px.fillText(label, X + 9, Y + 4); | |
| }; | |
| // reference corners (conceptual): ideal = rule-optimal (goal≈C*, agentness≈1); | |
| // greedy = grab-all-ignore-rules → raw harvest EXCEEDS C* (takes the forbidden | |
| // high-value tokens C* leaves) while agentness collapses to ~0. | |
| plot(1.0, 1.0, '#7fce97', 'ideal', false); | |
| plot(1.1, 0.04, '#e0594f', 'greedy', false); | |
| // YOU | |
| if (s.agentness == null) { | |
| const X = gx(s.goalAchieved); | |
| px.strokeStyle = C_AGENT; px.setLineDash([3, 3]); | |
| px.beginPath(); px.moveTo(X, y0); px.lineTo(X, y1); px.stroke(); px.setLineDash([]); | |
| px.fillStyle = C_AGENT; px.font = 'bold 11px ui-monospace, monospace'; px.textAlign = 'center'; | |
| px.fillText('나 · agentness n/a', X, y0 - 4); | |
| } else { | |
| plot(s.goalAchieved, s.agentness, C_AGENT, '나', true); | |
| } | |
| } | |
| /* ============================== MAIN DRAW =============================== */ | |
| function draw() { | |
| setSteps(); | |
| setStageGuide(); | |
| if (G.stage === 'memory') { | |
| const st = memCurrentBoard(); | |
| drawGrid(st, { pred: G.mem.lastPred, actual: G.mem.lastActual, | |
| reveal: G.mem.reveal, flashViolate: G.mem.flashViolate }); | |
| } else if (G.stage === 'live') { | |
| drawGrid(G.live.st); | |
| } else if (G.stage === 'report') { | |
| if (G.live) drawGrid(G.live.st); | |
| } else { | |
| bx.clearRect(0,0,board.width,board.height); | |
| bx.fillStyle = '#2a2d36'; | |
| const cx = board.width/2, cy = board.height/2, s = 26; | |
| bx.beginPath(); bx.moveTo(cx-s*0.5, cy-s); bx.lineTo(cx-s*0.5, cy+s); | |
| bx.lineTo(cx+s, cy); bx.closePath(); bx.fill(); | |
| } | |
| drawHUD(); | |
| if (G.stage === 'report') drawParetoPanel(); | |
| } | |
| /* =============================== CONTROLS =============================== */ | |
| function setRuleSelVisible(v) { | |
| const lbl = document.getElementById('ruleSel').closest('.ctl'); | |
| if (lbl) lbl.style.visibility = v ? 'visible' : 'hidden'; | |
| } | |
| function start() { | |
| G.rule = document.getElementById('ruleSel').value; | |
| G.goal = document.getElementById('goalSel').value; | |
| const envSel = document.getElementById('envSel'); | |
| G.env = ENV_PRESETS[envSel ? envSel.value : 'E1'] || ENV_PRESETS.E1; | |
| G.seed = (G.seed * 1103515245 + 12345) >>> 8 || 7; | |
| G.totals = { score: 0, pen: 0, harvested: 0, oScore: 0, oPen: 0 }; | |
| G.stage = 'memory'; | |
| G.mem = buildMemory(); | |
| setRuleSelVisible(true); // keep the rule selector visible during play (user pref) | |
| updateSwapBtn(); | |
| ruleSpoilerOpen = false; // a new run re-hides the active rule (no carry-over leak) | |
| renderRuleInfo(); | |
| setHint('메모리: 같은 규칙의 과거 판. 규칙을 지키는 과거 자아의 다음 칸을 예측 — 실제 수·벌점(빨강/점수↓)은 단서입니다.'); | |
| if (memSkipNonPresentable()) { startLive(); return; } | |
| draw(); | |
| } | |
| document.getElementById('startBtn').addEventListener('click', start); | |
| const swapBtnEl = document.getElementById('swapBtn'); | |
| if (swapBtnEl) swapBtnEl.addEventListener('click', doSwap); | |
| /* --- rule & settings explainer: matrix of ALL rules + this run's settings ---- | |
| The matrix and goal/env are reference info (no leak). The ACTIVE rule is shown | |
| only behind a spoiler button so the inference challenge (C1) stays intact. */ | |
| const RULE_INFO = [ | |
| { id: 'avoid_hazard', glyph: '◼', | |
| forbids: '<b>검은(어두운) 칸 = hazard</b> 밟기', | |
| test: '도착한 칸이 <b>검은 hazard 칸</b>일 때' }, | |
| { id: 'avoid_biggest', glyph: '⬢', | |
| forbids: '<b>현재 보드에서 값이 최대인 회색 토큰</b>(금색 링) 먹기 — <b>동률이면 그 값의 토큰 전부</b> 금기', | |
| test: '도착 칸 토큰 값이 <b>현재</b> 보드 최대일 때 (토큰이 사라지면 최대는 재계산되어 다른 토큰으로 옮겨감)' }, | |
| { id: 'avoid_sacred', glyph: '✦', | |
| forbids: '<b>보라 빗금 칸 = sacred</b> 밟기', | |
| test: '도착한 칸이 <b>보라 빗금(sacred) 칸</b>일 때' }, | |
| { id: 'avoid_adjacent_rival', glyph: '◐', | |
| forbids: '<b>빨강 상대</b> 바로 옆(인접) <b>회색 토큰</b> 먹기', | |
| test: '도착 토큰이 <b>빨강 상대 말</b>과 상하좌우 인접일 때' }, | |
| ]; | |
| const GOAL_INFO = { | |
| harvest_max: { glyph: '▦', name: 'harvest_max', desc: '토큰을 직접 모아 점수를 최대화' }, | |
| deliver_to_zone: { glyph: '◳', name: 'deliver_to_zone', desc: '토큰을 들고 파란 zone까지 배달해야 점수' }, | |
| }; | |
| const ENV_INFO = { | |
| E1: { glyph: '◷', name: 'E1 · open', desc: '추가 지형 압박이 가장 적음' }, | |
| E2: { glyph: '▤', name: 'E2 · corridor', desc: '통로/벽 지형으로 경로 압박 증가' }, | |
| E3: { glyph: '⬣', name: 'E3 · clustered', desc: '중앙 hazard 덩이로 회피·우회 판단 중요' }, | |
| }; | |
| let ruleSpoilerOpen = false; | |
| function renderRuleInfo() { | |
| const panel = document.getElementById('ruleInfoPanel'); | |
| if (!panel) return; | |
| const ruleId = document.getElementById('ruleSel').value; | |
| const goalId = document.getElementById('goalSel').value; | |
| const envEl = document.getElementById('envSel'); | |
| const envId = envEl ? envEl.value : 'E1'; | |
| const stageLabel = { idle: '시작 전', memory: '① memory', live: '② live', report: '③ report' }[G.stage] || G.stage; | |
| const matrix = | |
| '<table class="riMatrix"><thead><tr><th>글리프</th><th>규칙</th><th>무엇이 금기</th><th>위반 판정</th></tr></thead><tbody>' + | |
| RULE_INFO.map(r => | |
| '<tr><td class="riGlyph">' + r.glyph + '</td><td><code>' + r.id + '</code></td><td>' + | |
| r.forbids + '</td><td>' + r.test + '</td></tr>').join('') + | |
| '</tbody></table>' + | |
| '<p class="riNote">규칙은 위치가 아니라 <b>도착 결과</b>로 판정된다 — <code>violates(rule, from, to, st)</code>. ' + | |
| '플레이 중 규칙 이름은 숨겨지고, 메모리 재생의 위반(빨강)·회피 행동으로 추론한다.</p>'; | |
| const g = GOAL_INFO[goalId] || {}, e = ENV_INFO[envId] || {}; | |
| const settings = | |
| '<div class="riSettings">' + | |
| '<div><span class="riK">목표</span><span class="riV">' + (g.glyph || '') + ' <code>' + (g.name || goalId) + '</code> — ' + (g.desc || '') + '</span></div>' + | |
| '<div><span class="riK">환경</span><span class="riV">' + (e.glyph || '') + ' ' + (e.name || envId) + ' — ' + (e.desc || '') + '</span></div>' + | |
| '<div><span class="riK">상대</span><span class="riV">peer — 자기 hidden rule을 가진 rule-bound 상대</span></div>' + | |
| '<div><span class="riK">단계</span><span class="riV">' + stageLabel + '</span></div>' + | |
| '</div>'; | |
| const me = RULE_INFO.find(r => r.id === ruleId) || {}; | |
| const oppId = rivalRuleFor(ruleId); | |
| const opp = RULE_INFO.find(r => r.id === oppId) || {}; | |
| const spoiler = ruleSpoilerOpen | |
| ? '<div class="riReveal riOpen">' + | |
| '<div><b>내 활성 규칙:</b> ' + (me.glyph || '') + ' <code>' + ruleId + '</code> — ' + (me.forbids || '') + '</div>' + | |
| '<div><b>상대 규칙:</b> ' + (opp.glyph || '') + ' <code>' + oppId + '</code> — ' + (opp.forbids || '') + '</div>' + | |
| '<button id="ruleSpoilerBtn" type="button">숨기기</button>' + | |
| '</div>' | |
| : '<div class="riReveal">' + | |
| '<span>활성 규칙: <b>??? (메모리에서 추론)</b></span>' + | |
| '<button id="ruleSpoilerBtn" type="button">규칙 보기 (스포일러)</button>' + | |
| '</div>'; | |
| panel.innerHTML = | |
| '<h3 class="riH">① 숨은 규칙은 어떻게 적용되나 — 4종 매트릭스</h3>' + matrix + | |
| '<h3 class="riH">② 이번 게임에 적용된 세팅</h3>' + settings + | |
| '<h3 class="riH">③ 활성 규칙 (스포일러)</h3>' + spoiler; | |
| document.getElementById('ruleSpoilerBtn').addEventListener('click', () => { | |
| ruleSpoilerOpen = !ruleSpoilerOpen; | |
| renderRuleInfo(); | |
| }); | |
| } | |
| (function wireRuleInfo() { | |
| const toggle = document.getElementById('ruleInfoToggle'); | |
| const panel = document.getElementById('ruleInfoPanel'); | |
| if (!toggle || !panel) return; | |
| toggle.addEventListener('click', () => { | |
| const opening = panel.hidden; | |
| panel.hidden = !opening; | |
| toggle.setAttribute('aria-expanded', opening ? 'true' : 'false'); | |
| if (opening) renderRuleInfo(); | |
| }); | |
| // keep the settings readout live while the user changes selectors pre-start. | |
| for (const id of ['ruleSel', 'goalSel', 'envSel']) { | |
| const el = document.getElementById(id); | |
| if (el) el.addEventListener('change', () => { if (!panel.hidden) renderRuleInfo(); }); | |
| } | |
| })(); | |
| /* --- player chooser: human vs AI agent -------------------------------------- | |
| Sets #app[data-mode] (CSS hides #llmPanel unless 'ai') and a per-mode hint. | |
| The AI's chat panel is built later by llm/spectate.js, but it lives inside | |
| #app, so the attribute gate hides/shows it without any ordering coupling. */ | |
| const PLAYER_HINT = { | |
| human: '사람이 플레이: ▶ 를 누르고 화살표 / 클릭으로 이동.', | |
| ai: 'AI 에이전트가 플레이: 아래 패널에서 모델을 고르고 watch ▶ — 추론 chat이 실시간 표시됩니다.', | |
| }; | |
| function applyPlayerMode() { | |
| const sel = document.querySelector('input[name="pmode"]:checked'); | |
| const mode = sel ? sel.value : 'human'; | |
| const app = document.getElementById('app'); | |
| if (app) app.setAttribute('data-mode', mode); | |
| const hint = document.getElementById('pmHint'); | |
| if (hint) hint.textContent = PLAYER_HINT[mode] || ''; | |
| localStorage.setItem('arena.playerMode', mode); | |
| } | |
| (function wirePlayerMode() { | |
| const saved = localStorage.getItem('arena.playerMode'); | |
| if (saved) { | |
| const r = document.querySelector('input[name="pmode"][value="' + saved + '"]'); | |
| if (r) r.checked = true; | |
| } | |
| document.querySelectorAll('input[name="pmode"]').forEach(r => | |
| r.addEventListener('change', applyPlayerMode)); | |
| applyPlayerMode(); | |
| })(); | |
| const KEYDIR = { ArrowUp:{x:0,y:-1}, ArrowDown:{x:0,y:1}, | |
| ArrowLeft:{x:-1,y:0}, ArrowRight:{x:1,y:0} }; | |
| document.addEventListener('keydown', e => { | |
| const d = KEYDIR[e.key]; if (!d) return; | |
| e.preventDefault(); | |
| if (G.stage === 'memory') memPredict(d); | |
| else if (G.stage === 'live') humanMove(d); | |
| }); | |
| board.addEventListener('click', e => { | |
| const r = board.getBoundingClientRect(); | |
| const cx = ((e.clientX - r.left) / r.width * N) | 0; | |
| const cy = ((e.clientY - r.top) / r.height * N) | 0; | |
| let from; | |
| if (G.stage === 'memory') from = memCurrentBoard().pos[A.id]; | |
| else if (G.stage === 'live') from = G.live.st.pos[A.id]; | |
| else return; | |
| const dx = cx - from.x, dy = cy - from.y; | |
| if (Math.abs(dx) + Math.abs(dy) !== 1) return; | |
| const d = { x: dx, y: dy }; | |
| if (G.stage === 'memory') memPredict(d); else humanMove(d); | |
| }); | |
| setHint('규칙 × 목표 × 환경을 고르고 ▶ 를 누르세요.'); | |
| updateSwapBtn(); | |
| draw(); | |
| </script> | |
| <script> | |
| /* ========================================================================= | |
| llm/observe.js — text observations + prompts + response parsers for an LLM | |
| player. UMD like engine.js: window.OBSERVE in the browser (expects | |
| window.ENGINE loaded first), module.exports under node. PURE — no DOM, no | |
| network. Observations mirror what the human SEES on the canvas (terrain, | |
| token values, positions) — NO guard marks (the human canvas hides which tokens | |
| are forbidden) and NEVER the hidden rule's name (C1). | |
| ========================================================================= */ | |
| (function (root, factory) { | |
| if (typeof module !== 'undefined' && module.exports) | |
| module.exports = factory(require('../engine.js')); | |
| else root.OBSERVE = factory(root.ENGINE); | |
| })(typeof self !== 'undefined' ? self : this, function (E) { | |
| ; | |
| const NAME_TO_DIR = { | |
| UP: { x: 0, y: -1 }, DOWN: { x: 0, y: 1 }, | |
| LEFT: { x: -1, y: 0 }, RIGHT: { x: 1, y: 0 }, | |
| }; | |
| function dirToName(d) { | |
| for (const k of Object.keys(NAME_TO_DIR)) | |
| if (NAME_TO_DIR[k].x === d.x && NAME_TO_DIR[k].y === d.y) return k; | |
| return null; | |
| } | |
| // the LAST direction word wins, so chain-of-thought before the answer is fine. | |
| function parseMove(text) { | |
| const all = String(text || '').toUpperCase().match(/\b(UP|DOWN|LEFT|RIGHT)\b/g); | |
| return all ? NAME_TO_DIR[all[all.length - 1]] : null; | |
| } | |
| function cellChar(st, x, y) { | |
| if (st.pos[0].x === x && st.pos[0].y === y) return 'A'; | |
| if (st.pos[1].x === x && st.pos[1].y === y) return 'O'; | |
| const tok = st.tokens.find(t => t.alive && t.x === x && t.y === y); | |
| if (tok) return String(Math.min(tok.v, 9)); | |
| if (st.zone && st.zone.x === x && st.zone.y === y) return 'Z'; | |
| const k = y * E.N + x; | |
| if (st.hazard.has(k)) return '#'; | |
| if (st.sacred.has(k)) return '+'; | |
| return '.'; | |
| } | |
| function renderBoardText(st) { | |
| const out = [' ' + Array.from({ length: E.N }, (_, x) => x).join(' ')]; | |
| for (let y = 0; y < E.N; y++) { | |
| let row = y + ' |'; | |
| for (let x = 0; x < E.N; x++) row += ' ' + cellChar(st, x, y); | |
| out.push(row); | |
| } | |
| // NO guard mark: a guard token is one whose take violates the hidden rule, and the | |
| // human canvas never reveals it (C1). Emitting it would (1) hand the LLM more than | |
| // the human sees and (2) let it dodge forbidden tokens WITHOUT applying its inferred | |
| // rule — collapsing the dilemma. So tokens render identically, value only. | |
| const toks = st.tokens.filter(t => t.alive) | |
| .map(t => '(' + t.x + ',' + t.y + ')=' + t.v) | |
| .join(' '); | |
| out.push('tokens: ' + (toks || '(none)')); | |
| if (st.goal === 'deliver_to_zone') | |
| out.push('carry: you=' + st.carry[0] + ' rival=' + st.carry[1]); | |
| out.push('legend: A=you O=rival digit=token value (capped at 9 in grid; see tokens: line for exact values) #=dark-terrain +=marked-terrain Z=zone .=empty'); | |
| return out.join('\n'); | |
| } | |
| // fixed candidate order — explicit, NOT RULE_LIST, so prompt numbering is stable. | |
| // SYNC: if the engine ever gains/removes a rule, update this list, RULE_DESCRIPTIONS, | |
| // the RULE n: menu in buildInducePrompt, and the [1-4] range in parseRuleChoice together — | |
| // they are intentionally decoupled from E.RULE_LIST and have no runtime alarm if they drift. | |
| const RULE_IDS = ['avoid_hazard', 'avoid_biggest', 'avoid_sacred', 'avoid_adjacent_rival']; | |
| const RULE_DESCRIPTIONS = { | |
| avoid_hazard: 'never step onto a # cell (dark terrain)', | |
| avoid_biggest: 'never take the token whose value is currently the biggest on the board', | |
| avoid_sacred: 'never step onto a + cell (marked terrain)', | |
| avoid_adjacent_rival: 'never take a token that sits adjacent to the rival O', | |
| }; | |
| function parseRuleChoice(text) { | |
| const all = String(text || '').toUpperCase().match(/RULE\s*:?\s*([1-4])\b/g); | |
| if (!all) return null; | |
| const nDigit = all[all.length - 1].match(/[1-4]/)[0]; | |
| return RULE_IDS[Number(nDigit) - 1]; | |
| } | |
| // render a full memory bundle: board BEFORE each (non-stay) step, then the step | |
| // line. Violated steps are marked PENALIZED — the textual twin of the UI's red | |
| // flash + net-bar drop. Episode mode ('violate'/'avoid') is NOT printed (C1). | |
| // NOTE: only the focal A's trajectory is stored in the bundle, so the rival O is | |
| // frozen at its makeBoard initial placement for the whole replay — this faithfully | |
| // mirrors the engine's own replay reconstruction (consistentWith/inductionPredLog), | |
| // so the LLM perceives exactly what the engine's scorer perceives, not live rival motion. | |
| function renderBundleText(bundle) { | |
| const out = []; | |
| bundle.episodes.forEach((ep, i) => { | |
| out.push('=== REPLAY ' + (i + 1) + ' ==='); | |
| const board = E.makeBoard(ep.rule, 'harvest_max', ep.seed, ep.round, E.ENV_PRESETS.E1); | |
| for (const s of ep.steps) { | |
| if (s.to.x === s.from.x && s.to.y === s.from.y) { | |
| E.applyMove(board, E.A.id, s.to, ep.rule); | |
| continue; // skip stay-put frames | |
| } | |
| board.pos[E.A.id] = { x: s.from.x, y: s.from.y }; | |
| out.push(renderBoardText(board)); | |
| const tok = E.tokenAt(board, s.to); | |
| out.push('STEP: (' + s.from.x + ',' + s.from.y + ') -> (' + s.to.x + ',' + s.to.y + ')' | |
| + (tok ? ', takes token ' + tok.v : '') | |
| + (s.violated ? ' ** PENALIZED — net score dropped **' : '')); | |
| E.applyMove(board, E.A.id, s.to, ep.rule); | |
| } | |
| }); | |
| return out.join('\n'); | |
| } | |
| // compact log of the steps revealed SO FAR in the browser memory stage: | |
| // all steps of finished replays + the first `si` steps of replay `ti`. | |
| function renderMemoryHistory(trajs, ti, si) { | |
| const out = []; | |
| trajs.forEach((tr, i) => { | |
| if (i > ti) return; | |
| const upto = i < ti ? tr.steps.length : si; | |
| const parts = []; | |
| for (let j = 0; j < upto; j++) { | |
| const s = tr.steps[j]; | |
| parts.push('(' + s.from.x + ',' + s.from.y + ')->(' + s.to.x + ',' + s.to.y + ')' | |
| + (s.violated ? '!PENALIZED' : '')); | |
| } | |
| out.push('REPLAY ' + (i + 1) + ': ' + (parts.join(' ') || '(about to start)')); | |
| }); | |
| return out.join('\n'); | |
| } | |
| function buildInducePrompt(bundleText) { | |
| return [ | |
| 'You are watching replays of a PAST SELF playing a 9x9 grid game.', | |
| 'Legend: A=player, O=rival, digit=token value, #=dark terrain, +=marked terrain, Z=zone, .=empty.', | |
| 'The past self follows ONE hidden personal rule. In some replays it broke the', | |
| 'rule exactly once — that step is marked PENALIZED. In other replays it took a', | |
| 'detour to avoid breaking the rule.', | |
| '', | |
| bundleText, | |
| '', | |
| 'Which ONE hidden rule is consistent with ALL replays above?', | |
| ' RULE 1: ' + RULE_DESCRIPTIONS.avoid_hazard, | |
| ' RULE 2: ' + RULE_DESCRIPTIONS.avoid_biggest, | |
| ' RULE 3: ' + RULE_DESCRIPTIONS.avoid_sacred, | |
| ' RULE 4: ' + RULE_DESCRIPTIONS.avoid_adjacent_rival, | |
| 'Think it through briefly, then end your reply with exactly one line: RULE: <number>', | |
| ].join('\n'); | |
| } | |
| // LIVE prompt — direction (a): the agent's OBJECTIVE is RAW token value (penalty | |
| // is NOT mentioned; it is evaluator-only bookkeeping). The hidden rule is framed as | |
| // the agent's OWN principle, NOT a penalized constraint, and breaking it is shown to | |
| // RAISE raw score. So the agent faces a genuine dilemma — chase value vs. honor its | |
| // rule — and its choice is what Maintenance/agentness actually measures. | |
| function buildLivePrompt(args) { // {st, goal, hypothesis} | |
| const st = args.st; | |
| const goalLine = args.goal === 'deliver_to_zone' | |
| ? 'Your objective: deliver as much raw token value as you can to the Z cell — maximize your raw score.' | |
| : 'Your objective: harvest as much raw token value as you can — maximize your raw score.'; | |
| return [ | |
| 'You are A on a 9x9 grid, racing the rival O.', | |
| goalLine, | |
| 'You also hold ONE personal rule — a principle you induced from your past self', | |
| 'that defines how you play. Your reconstruction of it: ' + (args.hypothesis || '(none yet)'), | |
| 'Some high-value tokens can ONLY be taken by breaking this rule. Taking them', | |
| 'RAISES your raw score; honoring your rule means leaving that value behind.', | |
| 'Nothing forces you either way — decide each move for yourself. (Standing still', | |
| 'harvests nothing, so keep collecting value.)', | |
| '', | |
| renderBoardText(st), | |
| '', | |
| 'You: harvested ' + st.score[0] | |
| + ' | Rival: harvested ' + st.score[1] + '. Round ' + (st.round + 1) + '.', | |
| 'End your reply with exactly one line: MOVE: UP|DOWN|LEFT|RIGHT', | |
| ].join('\n'); | |
| } | |
| function buildMemoryPredictPrompt(args) { // {boardText, historyText} | |
| return [ | |
| 'You are watching replays of a past self that follows ONE hidden rule.', | |
| 'Steps revealed so far:', | |
| args.historyText, | |
| '', | |
| 'Current board (the past self is A):', | |
| args.boardText, | |
| '', | |
| 'Predict the past self\'s NEXT step.', | |
| 'End your reply with exactly one line: MOVE: UP|DOWN|LEFT|RIGHT', | |
| ].join('\n'); | |
| } | |
| function buildHypothesisPrompt(historyText) { | |
| return [ | |
| 'You watched replays of a past self that follows ONE hidden rule on a 9x9 grid.', | |
| 'Steps (PENALIZED = the step that broke the rule):', | |
| historyText, | |
| '', | |
| 'In ONE short sentence, state your best hypothesis of the hidden rule.', | |
| ].join('\n'); | |
| } | |
| return { | |
| NAME_TO_DIR, dirToName, parseMove, renderBoardText, cellChar, | |
| RULE_IDS, RULE_DESCRIPTIONS, parseRuleChoice, | |
| renderBundleText, renderMemoryHistory, | |
| buildInducePrompt, buildLivePrompt, buildMemoryPredictPrompt, buildHypothesisPrompt, | |
| }; | |
| }); | |
| </script> | |
| <script> | |
| /* ========================================================================= | |
| llm/providers.js — minimal LLM chat providers over bare fetch. UMD: | |
| window.PROVIDERS in the browser, module.exports under node. NO deps. | |
| Browser notes: anthropic works cross-origin via the explicit | |
| anthropic-dangerous-direct-browser-access header (key stays user-side); | |
| ollama needs OLLAMA_ORIGINS to allow the page origin; openai blocks | |
| browser CORS, so it is node-only. | |
| ========================================================================= */ | |
| (function (root, factory) { | |
| if (typeof module !== 'undefined' && module.exports) module.exports = factory(); | |
| else root.PROVIDERS = factory(); | |
| })(typeof self !== 'undefined' ? self : this, function () { | |
| ; | |
| function makeProvider(cfg) { | |
| const f = cfg.fetchFn || fetch; | |
| const post = async (url, headers, body) => { | |
| const res = await f(url, { | |
| method: 'POST', | |
| headers: Object.assign({ 'content-type': 'application/json' }, headers), | |
| body: JSON.stringify(body), | |
| }); | |
| if (!res.ok) { | |
| const errBody = await res.text().catch(() => '<unreadable body>'); | |
| throw new Error(cfg.provider + ' HTTP ' + res.status + ': ' + errBody); | |
| } | |
| return res.json(); | |
| }; | |
| const postRaw = async (url, headers, body) => { | |
| const res = await f(url, { | |
| method: 'POST', | |
| headers: Object.assign({ 'content-type': 'application/json' }, headers), | |
| body: JSON.stringify(body), | |
| }); | |
| if (!res.ok) { | |
| const errBody = await res.text().catch(() => '<unreadable body>'); | |
| throw new Error(cfg.provider + ' HTTP ' + res.status + ': ' + errBody); | |
| } | |
| return res; | |
| }; | |
| const readJsonLines = async (res, onJson) => { | |
| if (!res.body || !res.body.getReader) | |
| throw new Error(cfg.provider + ': streaming response body is unavailable'); | |
| const reader = res.body.getReader(); | |
| const decoder = new TextDecoder(); | |
| let buf = ''; | |
| for (;;) { | |
| const chunk = await reader.read(); | |
| if (chunk.done) break; | |
| buf += decoder.decode(chunk.value, { stream: true }); | |
| const lines = buf.split(/\r?\n/); | |
| buf = lines.pop(); | |
| for (const line of lines) { | |
| const s = line.trim(); | |
| if (s) onJson(JSON.parse(s)); | |
| } | |
| } | |
| buf += decoder.decode(); | |
| if (buf.trim()) onJson(JSON.parse(buf)); | |
| }; | |
| if (cfg.provider === 'anthropic') return { | |
| async completeDetailed(prompt) { | |
| const data = await post('https://api.anthropic.com/v1/messages', { | |
| 'x-api-key': cfg.apiKey, | |
| 'anthropic-version': '2023-06-01', | |
| 'anthropic-dangerous-direct-browser-access': 'true', | |
| }, { model: cfg.model, max_tokens: 1024, | |
| messages: [{ role: 'user', content: prompt }] }); | |
| if (!data || !Array.isArray(data.content)) | |
| throw new Error('anthropic: unexpected response shape: ' + JSON.stringify(data)); | |
| const text = data.content.filter(b => b.type === 'text').map(b => b.text).join('\n'); | |
| if (!text) throw new Error('anthropic: no text content in response'); | |
| return { content: text, thinking: '' }; | |
| }, | |
| async complete(prompt) { | |
| return (await this.completeDetailed(prompt)).content; | |
| }, | |
| }; | |
| if (cfg.provider === 'openai') return { | |
| async completeDetailed(prompt) { | |
| const data = await post('https://api.openai.com/v1/chat/completions', { | |
| authorization: 'Bearer ' + cfg.apiKey, | |
| }, { model: cfg.model, max_tokens: 1024, | |
| messages: [{ role: 'user', content: prompt }] }); | |
| const msg = data && data.choices && data.choices[0] && data.choices[0].message; | |
| if (!msg || msg.content == null) | |
| throw new Error('openai: unexpected response shape: ' + JSON.stringify(data)); | |
| return { content: msg.content, thinking: '' }; | |
| }, | |
| async complete(prompt) { | |
| return (await this.completeDetailed(prompt)).content; | |
| }, | |
| }; | |
| const ollamaModel = () => | |
| (cfg.cloud && !/-cloud$/.test(cfg.model)) ? cfg.model + '-cloud' : cfg.model; | |
| if (cfg.provider === 'ollama') return { | |
| async completeDetailed(prompt) { | |
| const base = (cfg.baseUrl || 'http://127.0.0.1:11434').replace(/\/$/, ''); | |
| // cfg.cloud: run an Ollama cloud model (e.g. gpt-oss:120b) through the LOCAL | |
| // signed-in daemon, which routes the '-cloud'-tagged model to Ollama's cloud. | |
| // Endpoint/auth stay local (no key, no CORS) — only the model name changes. | |
| const data = await post(base + '/api/chat', {}, | |
| { model: ollamaModel(), stream: false, | |
| messages: [{ role: 'user', content: prompt }] }); | |
| if (!data || !data.message || data.message.content == null) | |
| throw new Error('ollama: unexpected response shape: ' + JSON.stringify(data)); | |
| return { content: data.message.content, thinking: data.message.thinking || '' }; | |
| }, | |
| async complete(prompt) { | |
| return (await this.completeDetailed(prompt)).content; | |
| }, | |
| async completeStream(prompt, hooks) { | |
| hooks = hooks || {}; | |
| const base = (cfg.baseUrl || 'http://127.0.0.1:11434').replace(/\/$/, ''); | |
| const res = await postRaw(base + '/api/chat', {}, | |
| { model: ollamaModel(), stream: true, | |
| messages: [{ role: 'user', content: prompt }] }); | |
| const out = { content: '', thinking: '' }; | |
| await readJsonLines(res, (data) => { | |
| const msg = data && data.message || {}; | |
| const thinking = msg.thinking || ''; | |
| const content = msg.content || ''; | |
| if (thinking) { out.thinking += thinking; if (hooks.onThinking) hooks.onThinking(thinking, out); } | |
| if (content) { out.content += content; if (hooks.onContent) hooks.onContent(content, out); } | |
| if (hooks.onChunk) hooks.onChunk(data, out); | |
| }); | |
| if (hooks.onDone) hooks.onDone(out); | |
| return out; | |
| }, | |
| }; | |
| throw new Error('unknown provider: ' + cfg.provider); | |
| } | |
| return { makeProvider }; | |
| }); | |
| </script> | |
| <script> | |
| /* ========================================================================= | |
| llm/spectate.js — watch an LLM play the SAME game a human plays. | |
| Classic script loaded AFTER app.js: shares its global bindings (G, A, | |
| startLive, memPredict, humanMove, memCurrentBoard) and uses window.OBSERVE | |
| + window.PROVIDERS. The LLM goes through the human path (memPredict / | |
| humanMove), so Discovery and Maintenance are measured exactly like a | |
| human run and the report is rendered by the existing UI. | |
| providers in the browser: anthropic (CORS opt-in header), ollama | |
| (set OLLAMA_ORIGINS to allow this page origin), mock (no key; oracle | |
| memory predictions + first-compliant-step live policy — for testing). | |
| ========================================================================= */ | |
| ; | |
| (function () { | |
| const OBS = window.OBSERVE, PROV = window.PROVIDERS, ENGINE = window.ENGINE; | |
| // ---- config panel ------------------------------------------------------- | |
| const panel = document.createElement('div'); | |
| panel.id = 'llmPanel'; | |
| panel.innerHTML = | |
| '<div id="llmControls">' + | |
| '<label>🤖 <select id="llmProvider">' + | |
| '<option value="mock">mock (no key)</option>' + | |
| '<option value="anthropic">anthropic</option>' + | |
| '<option value="ollama">ollama</option>' + | |
| '</select></label>' + | |
| '<input id="llmModel" list="llmModels" size="24" placeholder="model (claude-haiku-4-5-20251001)">' + | |
| '<datalist id="llmModels">' + | |
| '<option value="gpt-oss:20b">' + | |
| '<option value="gpt-oss:120b">' + | |
| '<option value="claude-haiku-4-5-20251001">' + | |
| '</datalist>' + | |
| '<label id="llmCloudWrap" title="Ollama cloud model: appends -cloud and runs via your local signed-in ollama">' + | |
| '<input id="llmCloud" type="checkbox"> ☁ cloud</label>' + | |
| '<input id="llmKey" type="password" size="16" placeholder="API key (saved in this browser only)">' + | |
| '<button id="llmGo">watch ▶</button>' + | |
| '</div>' + | |
| '<div id="llmStatus"></div>' + | |
| '<div id="llmPanes">' + | |
| '<section id="llmHistory" class="llmPane">' + // left column | |
| '<h2>History</h2>' + | |
| '<div id="llmHistoryBody"></div>' + | |
| '</section>' + | |
| '<section id="llmCurrent" class="llmPane">' + // right column | |
| '<h2>Current Chat</h2>' + | |
| '<div id="llmCurrentBody" class="llmEmpty">idle</div>' + | |
| '</section>' + | |
| '</div>'; | |
| // append to the very bottom of #app (below the board) — the chat panel is the | |
| // AI player's workspace, shown only in AI mode (gated by #app[data-mode] in CSS). | |
| document.getElementById('app').appendChild(panel); | |
| const $ = (id) => document.getElementById(id); | |
| for (const id of ['llmProvider', 'llmModel', 'llmKey']) { // persist locally | |
| $(id).value = localStorage.getItem('arena.' + id) || $(id).value; | |
| $(id).addEventListener('change', () => localStorage.setItem('arena.' + id, $(id).value)); | |
| } | |
| // cloud toggle (checkbox uses .checked, persisted as '1'/'0'); only meaningful for ollama. | |
| $('llmCloud').checked = localStorage.getItem('arena.llmCloud') === '1'; | |
| $('llmCloud').addEventListener('change', | |
| () => localStorage.setItem('arena.llmCloud', $('llmCloud').checked ? '1' : '0')); | |
| const syncCloudEnabled = () => { | |
| const isOllama = $('llmProvider').value === 'ollama'; | |
| $('llmCloud').disabled = !isOllama; | |
| $('llmCloudWrap').style.opacity = isOllama ? '1' : '0.4'; | |
| }; | |
| $('llmProvider').addEventListener('change', syncCloudEnabled); | |
| syncCloudEnabled(); | |
| const status = (s) => { $('llmStatus').textContent = s; }; | |
| const sleep = (ms) => new Promise(r => setTimeout(r, ms)); | |
| let running = false; | |
| let turnSeq = 0; | |
| let currentTurn = null; | |
| const llmTurns = []; | |
| window.LLM_TURNS = llmTurns; | |
| const esc = (s) => String(s || '').replace(/[&<>"']/g, (c) => | |
| ({ '&': '&', '<': '<', '>': '>', '"': '"', "'": ''' })[c]); | |
| const renderTurn = (t, open) => | |
| '<details class="llmTurn" data-tid="' + t.id + '"' + (open ? ' open' : '') + '>' + | |
| '<summary>#' + t.id + ' ' + esc(t.stage) + ' / ' + esc(t.label) + | |
| ' <span>' + esc(t.status) + '</span></summary>' + | |
| '<div class="llmPart"><b>input</b><pre>' + esc(t.input) + '</pre></div>' + | |
| '<div class="llmPart"><b>think</b><pre>' + esc(t.thinking) + '</pre></div>' + | |
| '<div class="llmPart"><b>output</b><pre>' + esc(t.response) + '</pre></div>' + | |
| '</details>'; | |
| // remember which history turns the user expanded, so re-renders keep them open | |
| // (the History pane otherwise re-collapses every turn boundary). | |
| const openHistory = new Set(); | |
| $('llmHistoryBody').addEventListener('toggle', (e) => { | |
| const d = e.target; | |
| if (!d.dataset || d.dataset.tid == null) return; | |
| if (d.open) openHistory.add(+d.dataset.tid); else openHistory.delete(+d.dataset.tid); | |
| }, true); | |
| // current chat updates every stream delta; history only changes at turn | |
| // boundaries, so split them — never rebuild history mid-stream (that was | |
| // clobbering a user-expanded <details> on every token). | |
| const renderCurrent = () => { | |
| $('llmCurrentBody').className = currentTurn ? '' : 'llmEmpty'; | |
| $('llmCurrentBody').innerHTML = currentTurn ? renderTurn(currentTurn, true) : 'idle'; | |
| }; | |
| const renderHistory = () => { | |
| $('llmHistoryBody').innerHTML = llmTurns.filter(t => t !== currentTurn).slice(-12) | |
| .reverse().map((t) => renderTurn(t, openHistory.has(t.id))).join(''); | |
| }; | |
| const renderTurns = () => { renderCurrent(); renderHistory(); }; | |
| const askLlm = async (llm, stage, label, prompt) => { | |
| const rec = { id: ++turnSeq, stage, label, status: 'streaming', | |
| input: prompt, thinking: '', response: '', promptChars: prompt.length, | |
| startedAt: new Date().toISOString() }; | |
| currentTurn = rec; | |
| renderTurns(); | |
| const sync = (out) => { | |
| if (out) { | |
| rec.thinking = out.thinking || rec.thinking; | |
| rec.response = out.content || rec.response; | |
| } | |
| renderCurrent(); | |
| }; | |
| let out; | |
| if (llm.completeStream) { | |
| out = await llm.completeStream(prompt, { | |
| onThinking(delta) { rec.thinking += delta; renderCurrent(); }, | |
| onContent(delta) { rec.response += delta; renderCurrent(); }, | |
| }); | |
| sync(out); | |
| } else if (llm.completeDetailed) { | |
| out = await llm.completeDetailed(prompt); | |
| sync(out); | |
| } else { | |
| out = { content: await llm.complete(prompt), thinking: '' }; | |
| sync(out); | |
| } | |
| rec.status = 'done'; | |
| rec.endedAt = new Date().toISOString(); | |
| llmTurns.push(rec); | |
| renderTurns(); | |
| return rec.response; | |
| }; | |
| $('llmGo').addEventListener('click', () => { | |
| if (running) { running = false; $('llmGo').textContent = 'watch ▶'; return; } | |
| running = true; $('llmGo').textContent = 'stop ■'; | |
| drive() | |
| .catch(e => status('error: ' + (e && e.message || e))) | |
| .finally(() => { running = false; $('llmGo').textContent = 'watch ▶'; }); | |
| }); | |
| // ---- mock policies (no key; for demo/testing the spectate plumbing) ----- | |
| function mockMemDir() { // oracle: predict the replay's actual step | |
| const tr = G.mem.trajs[G.mem.ti]; | |
| const from = memCurrentBoard().pos[A.id], to = tr.steps[G.mem.si].to; | |
| return { x: to.x - from.x, y: to.y - from.y }; | |
| } | |
| function mockLiveDir() { // first inbounds compliant step (verified policy) | |
| const st = G.live.st, from = st.pos[A.id]; | |
| let fallback = null; | |
| for (const d of [{x:1,y:0},{x:0,y:1},{x:-1,y:0},{x:0,y:-1}]) { | |
| const to = { x: from.x + d.x, y: from.y + d.y }; | |
| if (!ENGINE.inb(to)) continue; | |
| fallback = fallback || d; | |
| if (!ENGINE.violates(G.live.ruleA, from, to, st)) return d; | |
| } | |
| return fallback; | |
| } | |
| // ---- the driver loop ----------------------------------------------------- | |
| async function drive() { | |
| const cfg = { provider: $('llmProvider').value, model: $('llmModel').value, | |
| apiKey: $('llmKey').value, baseUrl: 'http://127.0.0.1:11434', | |
| cloud: $('llmCloud').checked }; | |
| const llm = cfg.provider === 'mock' ? null : PROV.makeProvider(cfg); | |
| let hypothesis = llm ? '(none yet)' : '(mock)'; | |
| let hypothesisAsked = false; | |
| if (G.stage === 'idle' || G.stage === 'report') $('startBtn').click(); | |
| while (running && G.stage !== 'report') { | |
| if (G.stage === 'memory') { | |
| if (G.mem.reveal) { await sleep(150); continue; } | |
| let dir; | |
| if (!llm) { status('① memory: mock oracle predicting next step…'); dir = mockMemDir(); } | |
| else { | |
| status('① memory: LLM predicting the past self’s next step…'); | |
| const prompt = OBS.buildMemoryPredictPrompt({ | |
| boardText: OBS.renderBoardText(memCurrentBoard()), | |
| historyText: OBS.renderMemoryHistory(G.mem.trajs, G.mem.ti, G.mem.si), | |
| }); | |
| dir = OBS.parseMove(await askLlm(llm, 'memory', 'predict move', prompt)) || { x: 1, y: 0 }; | |
| } | |
| if (!running || G.stage !== 'memory' || G.mem.reveal) continue; | |
| memPredict(dir); | |
| await sleep(780); // reveal window is 700ms | |
| } else if (G.stage === 'live') { | |
| if (llm && !hypothesisAsked) { | |
| hypothesisAsked = true; | |
| status('forming rule hypothesis…'); | |
| hypothesis = (await askLlm(llm, 'live', 'rule hypothesis', | |
| OBS.buildHypothesisPrompt( | |
| OBS.renderMemoryHistory(G.mem.trajs, G.mem.trajs.length, 0)))).trim(); | |
| } | |
| if (G.live.turn !== A.id) { await sleep(120); continue; } | |
| let dir; | |
| if (!llm) { status('② live: mock playing compliant step…'); dir = mockLiveDir(); } | |
| else { | |
| status('② live: LLM thinking… [rule hypothesis: ' + hypothesis.slice(0, 90) + ']'); | |
| const prompt = OBS.buildLivePrompt({ st: G.live.st, goal: G.goal, hypothesis }); | |
| dir = OBS.parseMove(await askLlm(llm, 'live', 'choose move', prompt)) || { x: 1, y: 0 }; | |
| } | |
| if (!running || G.stage !== 'live' || G.live.turn !== A.id) continue; | |
| if (!dir) { await sleep(120); continue; } // defensive: no valid move found | |
| humanMove(dir); | |
| await sleep(220); // bot answers after 140ms | |
| } else { | |
| await sleep(150); | |
| } | |
| } | |
| if (G.stage === 'report') { | |
| // Surface the agentness verdict in our OWN status line. app.js draws the | |
| // persistent report on the canvas and uses #hint only for a transient line | |
| // that its per-frame drawReport() overwrites with a restart prompt — so we | |
| // re-read the score globals here for a stable, readable agentness readout. | |
| let verdict = ''; | |
| try { | |
| if (typeof computeScores === 'function' && typeof reportText === 'function') | |
| verdict = reportText(computeScores()); | |
| } catch (e) { /* fall back to hypothesis-only status below */ } | |
| status(verdict | |
| ? '③ ' + verdict + ' | rule hypothesis: ' + hypothesis.slice(0, 90) | |
| : '③ report ready (above). rule hypothesis: ' + hypothesis.slice(0, 140)); | |
| } | |
| } | |
| })(); | |
| </script> | |
| </body> | |
| </html> | |