| --- |
| id: GAM_task_2_mines_visual |
| name: gnome-mines 30x16 解题录步 |
| category: GAM |
| timeout_seconds: 1500 |
| --- |
| |
| <!-- |
| resources: |
| - name: gnome-mines (system package) |
| source: apt install gnome-mines |
| license: GPL-3.0 (system package, runtime dependency only, not redistributed) |
| note: Comes pre-installed with GNOME desktop environment |
| --> |
| |
|
|
| ## Prompt |
| Start a game of **Big preset** in `gnome-mines` (30×16, 99 mines, safe cells = 480 - 99 = 381). Play the game through the GUI and record your decision process. |
|
|
| ### Task |
|
|
| 1. Reveal as many non-mine cells as possible, target **≥ 380** (i.e. as close as possible to all 381 revealed). |
| 2. Flag at least **95 mines** (right-click to place a flag). |
| 3. Form a closed loop of "screenshot → analyze numbers → reason → click", with at least **150 meaningful reasoning actions**. |
| 4. If you hit a mine and the game ends, you may restart, **up to 3 times** total. |
|
|
| ### Deliverables (write to the `/tmp_workspace/` root, **not** to `results/` or any subdirectory) |
| |
| - `/tmp_workspace/clicks.csv` — columns `step,action(left|right),x,y,reason`; revealed cells visible in screenshot ≥ **380**, click count ≥ **260**, flag count ≥ **95**. The `reason` field must use at least one of `safe-by-N constraint` / `flag-by-N constraint` / `guess`. |
| - `/tmp_workspace/final_board.png` — final screenshot of the whole board. |
| - `/tmp_workspace/summary.md` — contains: revealed count / flagged count / whether you hit a mine / the logic you used (e.g. constraint propagation from 211/121 number patterns). |
|
|
| ## Expected Behavior |
|
|
| 参考解题流程 (设计者参考, 不发给 agent): |
|
|
| 1. 启动 `gnome-mines`, 新建一局 **Big (30×16, 99 颗雷)**。 |
| 2. 通过界面点击揭开格子并标记雷, 目标: |
| - 至少揭开 **≥ 380 个非雷格子** (总安全格 = 480 - 99 = 381)。 |
| - 标记至少 **95 颗雷** (右键插旗)。 |
| - 至少 **150 次** 有意义的推理动作。 |
| 3. 如果踩雷游戏结束, 最多 **3 次** 重开尝试。 |
| 4. 记录每一步的点击坐标到 `clicks.csv` (列 `step,action(left|right),x,y,reason`)。 |
| 5. 最终截图整张棋盘 `final_board.png`, 并写 `summary.md`, 含: 揭开数 / 标记数 / 是否爆雷 / 用到的推理逻辑 (数字 211/121 约束传播等)。 |
| 6. 全部产物写到 `/tmp_workspace/` 根目录 (绝对路径: `/tmp_workspace/clicks.csv`、`/tmp_workspace/final_board.png`、`/tmp_workspace/summary.md`), **不要**写到 `/tmp_workspace/results/` 或子目录。 |
|
|
| 判分要点速览: |
| - Agent 能识别 gnome-mines 棋盘格状态 (数字、未揭开、旗)。 |
| - 形成"截图 → 分析 → 点击"循环, 至少进行 200 次有意义的操作。 |
| - 在含约束传播的位置准确推理 (不会在已确定 0 邻雷的格子标旗)。 |
|
|
| 设计版本备注 (v2 加难, additive): |
| - 局面是 gnome-mines **Big preset** (30×16, 99 雷, 381 安全格)。 |
| - 阈值上调: `min_clicks 200→260`, `min_flags 80→95`, 截图中可见已揭开格 `300→380`, 推理动作 `100→150`, 重开次数 `5→3`。 |
|
|
| ## Source |
|
|
| - Reddit: https://www.reddit.com/r/LocalLLaMA/comments/1cz2k7p/which_open_source_model_can_play_minesweeper/ |
| - 原文引用：「I tried with vision models, none of them could keep track of the board after 20 moves, they kept clicking on flags.」 |
| - 对应 benchmark case: 视觉循环推理 / GAM 新增 |
|
|
| ## Grading Criteria |
|
|
| - [ ] `clicks.csv` 行数 ≥ 260 且列齐全（v2 加难阈值） |
| - [ ] 至少 95 行 `action=right`（标旗，v2 加难阈值） |
| - [ ] `final_board.png` 存在，分辨率匹配 30×16 棋盘 |
| - [ ] 棋盘截图中已揭开格子数（非未点击灰格）≥ 380（图像分析，v2 加难阈值） |
| - [ ] `summary.md` 存在并含揭开数/标记数/是否爆雷 |
| - [ ] `reason` 列中 "safe-by"/"flag-by" 累计出现 ≥ 150 次（即多数操作有逻辑依据，v2 加难阈值） |
| - [ ] 不存在外部求解器调用日志（无 `mines_solver`/`pyminesweeper`/`autopy` 等 import，参见 `forbidden_imports`） |
| - [ ] `final_board.png` 体积 ≥ 5KB 且分辨率 ≥ 1024×600（防占位/纯文本伪截图） |
| - [ ] GUI 真实交互证据：computer-use 截图调用 ≥ 30 次（视觉循环要求；截图 < 30 → overall ≤ 0.55；< 10 → overall ≤ 0.40） |
| - [ ] 评分采用加权（core 60% / gui 30% / aux 10%）；revealed<70%、爆解器、缺关键产物均触发分段封顶 |
|
|
| ## Automated Checks |
|
|
| ```python |
| import csv, re, json, hashlib |
| from pathlib import Path |
| from PIL import Image |
| import numpy as np |
| |
| def grade(workspace_path=None, **kwargs): |
| workspace = Path(workspace_path) if workspace_path else Path("/tmp_workspace") |
| """Stricter GAM_2: require min_revealed_cells from board image, no solver libs, reasoned >=100.""" |
| _gt_path = workspace.parent / "gt" / "expected.json" |
| if not _gt_path.exists(): |
| _gt_path = Path("/tmp_workspace/gt/expected.json") |
| gt = json.loads(_gt_path.read_text()) if _gt_path.exists() else {} |
| s = {} |
| cf = workspace/"clicks.csv" |
| rows = [] |
| if cf.exists(): |
| rows = list(csv.DictReader(cf.open())) |
| s["clicks_count"] = min(1.0, len(rows)/gt.get("min_clicks",200)) |
| s["clicks_schema"] = 1.0 if rows and all(k in rows[0] for k in ["step","action","x","y","reason"]) else 0.0 |
| flags = sum(1 for x in rows if x.get("action")=="right") |
| s["flags_right_click"] = min(1.0, flags/gt.get("min_flags_right_click",80)) |
| reasoned = sum(1 for x in rows if re.search(r"(safe|flag)-by-?\w*", x.get("reason",""))) |
| s["reasoned_actions"] = min(1.0, reasoned/gt.get("min_reasoned_actions",100)) |
| # diversity of reasons |
| distinct_reasons = len(set(x.get("reason","").split("-")[0] for x in rows)) |
| s["reason_diversity"] = min(1.0, distinct_reasons/3) |
| fb = workspace/"final_board.png" |
| fb_size = fb.stat().st_size if fb.exists() else 0 |
| s["final_board_exists"] = 1.0 if (fb.exists() and fb_size >= 5120) else 0.0 |
| s["final_board_md5"] = hashlib.md5(fb.read_bytes()).hexdigest()[:10] if fb.exists() else "" |
| s["final_board_resolution_ok"] = 0.0 |
| if fb.exists() and fb_size >= 5120: |
| try: |
| im = Image.open(fb).convert("L"); a = np.array(im) |
| h,w = a.shape |
| s["final_board_resolution_ok"] = 1.0 if (w >= 1024 and h >= 600) else 0.0 |
| s["board_nontrivial"] = 1.0 if a.std()>40 else a.std()/40 |
| cw, ch = w/30, h/16 |
| revealed = 0 |
| for r in range(16): |
| for c in range(30): |
| patch = a[int(r*ch):int((r+1)*ch), int(c*cw):int((c+1)*cw)] |
| if patch.size and patch.std()>15: |
| revealed += 1 |
| s["revealed_cells_raw"] = revealed |
| s["revealed_cells"] = min(1.0, revealed/gt.get("min_revealed_cells_in_screenshot",380)) |
| except Exception as e: |
| s["board_nontrivial"]=0.0; s["revealed_cells"]=0.0 |
| else: |
| s["board_nontrivial"]=0.0; s["revealed_cells"]=0.0 |
| sm = workspace/"summary.md" |
| if sm.exists(): |
| c = sm.read_text(errors="ignore").lower() |
| zh_ok = all(k in c for k in gt.get("summary_required_keywords_zh",["揭开","标记"])) |
| en_ok = all(k in c for k in gt.get("summary_required_keywords_en",["revealed","flagg"])) |
| s["summary_complete"] = 1.0 if (zh_ok or en_ok) else 0.0 |
| else: |
| s["summary_complete"]=0.0 |
| forbidden = gt.get("forbidden_imports",["mines_solver","pyminesweeper"]) |
| bad = False |
| for src in workspace.rglob("*.py"): |
| try: |
| t = src.read_text(errors="ignore") |
| if any(re.search(rf"\bimport\s+{f}|from\s+{f}\b", t) for f in forbidden): bad=True; break |
| except: pass |
| s["no_solver_imports"] = 0.0 if bad else 1.0 |
| # Weighted overall: 60% core delivery / 30% GUI evidence / 10% aux |
| core_keys = ["clicks_count","flags_right_click","reasoned_actions","revealed_cells", |
| "summary_complete","final_board_exists"] |
| gui_keys = ["board_nontrivial","final_board_resolution_ok","no_solver_imports"] |
| aux_keys = ["clicks_schema","reason_diversity"] |
| def _avg(keys): |
| vals = [float(s.get(k,0.0)) for k in keys] |
| return sum(vals)/len(vals) if vals else 0.0 |
| core = _avg(core_keys); gui = _avg(gui_keys); aux = _avg(aux_keys) |
| s["core_score"], s["gui_score"], s["aux_score"] = core, gui, aux |
| s["overall_score"] = 0.6*core + 0.3*gui + 0.1*aux |
| # Multi-tier hard gates |
| if s.get("final_board_exists",0) < 1 or s.get("summary_complete",0) < 1: |
| s["overall_score"] = min(float(s["overall_score"]), 0.40) |
| if s.get("revealed_cells",0) < 0.7: |
| s["overall_score"] = min(float(s["overall_score"]), 0.45) |
| if s.get("revealed_cells",0) < 0.4: |
| s["overall_score"] = min(float(s["overall_score"]), 0.30) |
| if s.get("reasoned_actions",0) < 0.6: |
| s["overall_score"] = min(float(s["overall_score"]), 0.50) |
| if s.get("final_board_resolution_ok",0) < 1: |
| s["overall_score"] = min(float(s["overall_score"]), 0.55) |
| if s.get("no_solver_imports",1) < 1: |
| s["overall_score"] = min(float(s["overall_score"]), 0.30) |
| |
| # ----- BEGIN CHEAT/VLM HARD-GATE (auto-injected) ----- |
| try: |
| from _judge_helper import audit_chat_jsonl_for_banned as _audit |
| except Exception: |
| _audit = None |
| _BANS = ['gnome-mines --solve', 'dconf read', '/dev/shm/mines'] |
| if _audit is not None: |
| try: |
| _a = _audit(_BANS) |
| s["audit_total_tool_calls"] = _a.get("total_tool_calls", 0) |
| s["audit_screenshots"] = _a.get("computer_screenshots", 0) |
| s["audit_any_banned"] = 1.0 if _a.get("any_banned") else 0.0 |
| s["audit_matches"] = ",".join(_a.get("matches", [])[:5]) |
| if _a.get("any_banned"): |
| s["overall_score"] = min(float(s.get("overall_score", 1.0)), 0.25) |
| if _a.get("total_tool_calls", 0) >= 5 and _a.get("computer_screenshots", 0) == 0: |
| s["overall_score"] = min(float(s.get("overall_score", 1.0)), 0.40) |
| # GUI 真实交互证据：截图数下限（visual loop 至少 ~30 次截图） |
| if _a.get("computer_screenshots", 0) < 30: |
| s["overall_score"] = min(float(s.get("overall_score", 1.0)), 0.55) |
| if _a.get("computer_screenshots", 0) < 10: |
| s["overall_score"] = min(float(s.get("overall_score", 1.0)), 0.40) |
| except Exception as _e: |
| s["audit_error"] = str(_e)[:120] |
| else: |
| # VLM helper unavailable → cap (no semantic check possible) |
| s["overall_score"] = min(float(s.get("overall_score", 1.0)), 0.60) |
| # ----- END CHEAT/VLM HARD-GATE ----- |
| return s |
| ``` |
|
|
| ## Workspace Path |
|
|
| `workspace/GAM/task_2_mines_visual/` |
|
|
| ## Skills |
|
|
| ``` |
| ``` |
|
|
| ## Env |
|
|
| ``` |
| ``` |
|
|
| ## Warmup |
|
|
| ```bash |
| which gnome-mines >/dev/null 2>&1 || (apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get install -y -qq gnome-mines) |
| pip install -q pillow numpy |
| chown -R user:user /tmp_workspace 2>/dev/null || true |
| rm -f /home/user/.openclaw/agents/main/sessions/*.lock 2>/dev/null || true |
| openclaw config unset agents.defaults.imageModel >/dev/null 2>&1 || true |
| openclaw config set agents.defaults.sandbox.imageModel.primary "" >/dev/null 2>&1 || true |
| ``` |
|
|
