diff --git "a/retrocausal_validation" "b/retrocausal_validation" new file mode 100644--- /dev/null +++ "b/retrocausal_validation" @@ -0,0 +1,2752 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +AGI KNOWLEDGE VALIDATION FRAMEWORK - UNIFIED PRODUCTION SYSTEM (v7.0) +Integration of Consciousness Integrity Engine with Retrocausal Analysis +Enhanced with Quantum Validation, Temporal Coherence, and Epistemic Grounding +""" + +import asyncio +import hashlib +import time +import numpy as np +import re +import json +from datetime import datetime, timedelta +from typing import Dict, Any, List, Optional, Tuple, DefaultDict, Union +from dataclasses import dataclass, field +from collections import deque, defaultdict +from enum import Enum +import scipy.stats as stats +from abc import ABC, abstractmethod +import logging +import uuid +import aiohttp +from functools import wraps +import gc +import psutil +import os + +# Configure comprehensive logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("AGI_Knowledge_Validator") + +# === ENHANCED ENUMERATIONS === +class ParadoxStatus(Enum): + STABLE = "stable" + NEAR_PARADOX = "near_paradox" + FULL_PARADOX = "full_paradox" + +class ReasoningMode(Enum): + DEDUCTIVE = "deductive" + INDUCTIVE = "inductive" + ABDUCTIVE = "abductive" + BAYESIAN = "bayesian" + CAUSAL = "causal" + QUANTUM = "quantum" + RETROCAUSAL = "retrocausal" + +class KnowledgeDomain(Enum): + SCIENCE = "science" + MATHEMATICS = "mathematics" + PHILOSOPHY = "philosophy" + HISTORY = "history" + MEDICINE = "medicine" + TECHNOLOGY = "technology" + SOCIAL_SCIENCE = "social_science" + CONSCIOUSNESS_STUDIES = "consciousness_studies" + SYMBOLIC_SYSTEMS = "symbolic_systems" + +class TemporalState(Enum): + STABLE = "stable" + PARADOX_DETECTED = "paradox_detected" + RETRO_INFLUENCE = "retro_influence" + TEMPORAL_COHERENCE = "temporal_coherence" + +# === ENHANCED DATA STRUCTURES === +@dataclass +class Evidence: + """Enhanced evidence with retrocausal and quantum properties""" + evidence_id: str + content: str + strength: float + reliability: float + source_quality: float + contradictory: bool = False + timestamp: str = field(default_factory=lambda: datetime.now().isoformat()) + domain: Optional[KnowledgeDomain] = None + quantum_entanglement: float = 0.0 + retrocausal_influence: float = 0.0 + temporal_coherence: float = 1.0 + metadata: Dict = field(default_factory=dict) + + def weighted_strength(self) -> float: + """Calculate comprehensive evidence strength""" + base_strength = self.strength * self.reliability * self.source_quality + quantum_factor = 1.0 + (self.quantum_entanglement * 0.2) + temporal_factor = self.temporal_coherence + retro_factor = 1.0 + (self.retrocausal_influence * 0.1) + + return base_strength * quantum_factor * temporal_factor * retro_factor + + def evidence_quality_score(self) -> float: + """Calculate overall evidence quality""" + return min(self.weighted_strength() * (1.0 - self.contradictory * 0.5), 1.0) + +@dataclass +class Artifact: + """Temporal and symbolic artifacts with retrocausal properties""" + artifact_type: str + symbolic_hash: str + epoch: int + retro_influence: str + temporal_state: TemporalState + content: Optional[str] = None + paradox_score: float = 0.0 + convergence_links: List[str] = field(default_factory=list) + metadata: Dict = field(default_factory=dict) + +@dataclass +class InfluenceEpoch: + """Historical influence points with temporal significance""" + epoch: int + label: str + influence_strength: float = 1.0 + domain: KnowledgeDomain = KnowledgeDomain.HISTORY + paradox_contribution: float = 0.0 + +@dataclass +class Inquiry: + """Enhanced inquiry with quantum-temporal properties""" + inquiry_id: str + inquiry_text: str + temporal_anchor: Optional[int] = None + paradox_score: float = 0.0 + retro_influence_peaks: List[InfluenceEpoch] = field(default_factory=list) + flagged_artifacts: List[Artifact] = field(default_factory=list) + convergence_hash: str = "" + paradox_status: ParadoxStatus = ParadoxStatus.STABLE + damping_applied: bool = False + quantum_superposition: List[str] = field(default_factory=list) + temporal_coherence: float = 1.0 + validation_timestamp: str = field(default_factory=lambda: datetime.now().isoformat()) + +@dataclass +class UniversalClaim: + """Comprehensive knowledge claim with multi-dimensional validation""" + claim_id: str + content: str + evidence_chain: List[Evidence] + reasoning_modes: List[ReasoningMode] + sub_domains: List[KnowledgeDomain] + causal_mechanisms: List[str] + expected_validity: Optional[float] = None + quantum_entanglement: float = 0.0 + retrocausal_links: List[str] = field(default_factory=list) + temporal_consistency: float = 1.0 + symbolic_resonance: float = 0.0 + + def evidence_summary(self) -> Dict[str, float]: + """Generate comprehensive evidence summary""" + if not self.evidence_chain: + return { + "count": 0.0, + "avg_strength": 0.0, + "avg_reliability": 0.0, + "contradictory_count": 0.0, + "quantum_entanglement": 0.0, + "temporal_coherence": 1.0 + } + + count = len(self.evidence_chain) + avg_strength = np.mean([e.weighted_strength() for e in self.evidence_chain]) + avg_reliability = np.mean([e.reliability for e in self.evidence_chain]) + contradictory_count = sum(1 for e in self.evidence_chain if e.contradictory) + quantum_entanglement = np.mean([e.quantum_entanglement for e in self.evidence_chain]) + temporal_coherence = np.mean([e.temporal_coherence for e in self.evidence_chain]) + + return { + "count": float(count), + "avg_strength": avg_strength, + "avg_reliability": avg_reliability, + "contradictory_count": float(contradictory_count), + "quantum_entanglement": quantum_entanglement, + "temporal_coherence": temporal_coherence + } + + def overall_confidence(self) -> float: + """Calculate overall claim confidence""" + evidence_summary = self.evidence_summary() + + if evidence_summary["count"] == 0: + return 0.1 + + base_confidence = ( + evidence_summary["avg_strength"] * 0.4 + + evidence_summary["avg_reliability"] * 0.3 + + (1.0 - evidence_summary["contradictory_count"] / evidence_summary["count"]) * 0.3 + ) + + # Apply quantum and temporal factors + quantum_factor = 1.0 + (self.quantum_entanglement * 0.1) + temporal_factor = self.temporal_consistency + symbolic_factor = 1.0 + (self.symbolic_resonance * 0.05) + + return min(base_confidence * quantum_factor * temporal_factor * symbolic_factor, 1.0) + +@dataclass +class ResearchResult: + content: str + sources: List[Dict] + confidence: float + domain: str + timestamp: str + quantum_entanglement: float = 0.0 + retrocausal_influence: float = 0.0 + metadata: Dict = field(default_factory=dict) + +@dataclass +class EvidenceItem: + content: str + evidence_type: str + source: str + reliability: float + timestamp: str + quantum_properties: Dict = field(default_factory=dict) + metadata: Dict = field(default_factory=dict) + +@dataclass +class TemporalAnalysis: + historical_similarity: float + cyclical_resonance: float + future_trajectory: Dict + anomalies: List[Dict] + coherence_score: float + paradox_detected: bool = False + retrocausal_influence: float = 0.0 + quantum_temporal_entanglement: float = 0.0 + +@dataclass +class EngineConfig: + max_analysis_depth: int = 5 + timeout_seconds: int = 45 + cache_enabled: bool = True + log_level: str = "INFO" + domains_to_analyze: List[KnowledgeDomain] = field(default_factory=lambda: [ + KnowledgeDomain.SCIENCE, + KnowledgeDomain.HISTORY, + KnowledgeDomain.SYMBOLIC_SYSTEMS, + KnowledgeDomain.CONSCIOUSNESS_STUDIES + ]) + security_validation: bool = True + performance_monitoring: bool = True + quantum_validation: bool = True + retrocausal_analysis: bool = True + paradox_detection: bool = True + +# === COMPONENT 1: QUANTUM-RETROCAUSAL VALIDATOR === +class QuantumRetrocausalValidator: + """Advanced validator integrating quantum mechanics and retrocausal analysis""" + + def __init__(self, performance_monitor=None): + self.quantum_states = self._initialize_quantum_states() + self.retrocausal_patterns = self._initialize_retrocausal_patterns() + self.paradox_detector = ParadoxDetector() + self.performance_monitor = performance_monitor + + if self.performance_monitor: + self.validate_claim = self.performance_monitor.track_performance(self.validate_claim) + + def _initialize_quantum_states(self) -> Dict: + """Initialize quantum validation states""" + return { + "superposition": { + "description": "Multiple truth states coexisting", + "validation_method": "quantum_interference", + "certainty_threshold": 0.7 + }, + "entanglement": { + "description": "Correlated evidence across domains", + "validation_method": "correlation_analysis", + "certainty_threshold": 0.8 + }, + "decoherence": { + "description": "Collapse to classical truth state", + "validation_method": "evidence_convergence", + "certainty_threshold": 0.9 + } + } + + def _initialize_retrocausal_patterns(self) -> Dict: + """Initialize retrocausal influence patterns""" + return { + "temporal_echoes": { + "description": "Future knowledge influencing past evidence", + "detection_method": "causal_reversal_analysis", + "significance_threshold": 0.6 + }, + "paradox_resolution": { + "description": "Self-consistent time-loop resolution", + "detection_method": "temporal_coherence_check", + "significance_threshold": 0.7 + }, + "retrocausal_inference": { + "description": "Evidence from future reference frames", + "detection_method": "bayesian_retrocausal_updating", + "significance_threshold": 0.5 + } + } + + async def validate_claim(self, claim: UniversalClaim, context: Dict = None) -> Dict: + """Comprehensive quantum-retrocausal validation""" + try: + validation_tasks = await asyncio.gather( + self._quantum_validation(claim), + self._retrocausal_analysis(claim, context), + self._paradox_detection(claim), + self._temporal_coherence_check(claim), + return_exceptions=True + ) + + # Process validation results + quantum_result = self._handle_validation_result(validation_tasks[0]) + retrocausal_result = self._handle_validation_result(validation_tasks[1]) + paradox_result = self._handle_validation_result(validation_tasks[2]) + temporal_result = self._handle_validation_result(validation_tasks[3]) + + # Calculate composite validation score + composite_score = self._calculate_composite_validation( + quantum_result, retrocausal_result, paradox_result, temporal_result + ) + + return { + "quantum_validation": quantum_result, + "retrocausal_analysis": retrocausal_result, + "paradox_detection": paradox_result, + "temporal_coherence": temporal_result, + "composite_validation_score": composite_score, + "validation_status": self._determine_validation_status(composite_score), + "quantum_entanglement": claim.quantum_entanglement, + "retrocausal_influence": self._calculate_retrocausal_influence(retrocausal_result), + "temporal_consistency": temporal_result.get("coherence_score", 0.5) + } + + except Exception as e: + logger.error(f"Quantum-retrocausal validation failed: {e}") + return { + "quantum_validation": {"error": str(e), "score": 0.3}, + "retrocausal_analysis": {"error": str(e), "score": 0.3}, + "paradox_detection": {"error": str(e), "score": 0.3}, + "temporal_coherence": {"error": str(e), "score": 0.3}, + "composite_validation_score": 0.3, + "validation_status": "validation_failed" + } + + def _handle_validation_result(self, result: Any) -> Dict: + """Handle validation results with error checking""" + if isinstance(result, Exception): + return {"error": str(result), "score": 0.3} + return result + + async def _quantum_validation(self, claim: UniversalClaim) -> Dict: + """Perform quantum mechanical validation""" + try: + evidence_summary = claim.evidence_summary() + + # Calculate quantum coherence + quantum_coherence = self._calculate_quantum_coherence(claim) + + # Assess superposition states + superposition_analysis = self._analyze_superposition(claim) + + # Evaluate quantum entanglement + entanglement_strength = self._evaluate_entanglement(claim) + + return { + "quantum_coherence": quantum_coherence, + "superposition_analysis": superposition_analysis, + "entanglement_strength": entanglement_strength, + "quantum_confidence": min((quantum_coherence + entanglement_strength) / 2, 1.0), + "validation_method": "quantum_mechanical_analysis" + } + except Exception as e: + logger.warning(f"Quantum validation failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _retrocausal_analysis(self, claim: UniversalClaim, context: Dict) -> Dict: + """Analyze retrocausal influences""" + try: + # Detect temporal echoes + temporal_echoes = self._detect_temporal_echoes(claim, context) + + # Analyze causal reversals + causal_reversals = self._analyze_causal_reversals(claim) + + # Calculate retrocausal influence + retro_influence = self._calculate_retrocausal_influence_metric(claim, temporal_echoes, causal_reversals) + + return { + "temporal_echoes": temporal_echoes, + "causal_reversals": causal_reversals, + "retrocausal_influence": retro_influence, + "analysis_confidence": min(retro_influence * 1.2, 1.0), + "temporal_anomalies": self._detect_temporal_anomalies(claim) + } + except Exception as e: + logger.warning(f"Retrocausal analysis failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _paradox_detection(self, claim: UniversalClaim) -> Dict: + """Detect and analyze temporal paradoxes""" + try: + return await self.paradox_detector.detect_paradoxes(claim) + except Exception as e: + logger.warning(f"Paradox detection failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _temporal_coherence_check(self, claim: UniversalClaim) -> Dict: + """Check temporal coherence and consistency""" + try: + coherence_score = self._calculate_temporal_coherence(claim) + consistency_check = self._verify_temporal_consistency(claim) + timeline_analysis = self._analyze_timeline_coherence(claim) + + return { + "coherence_score": coherence_score, + "consistency_check": consistency_check, + "timeline_analysis": timeline_analysis, + "overall_temporal_health": min((coherence_score + consistency_check) / 2, 1.0) + } + except Exception as e: + logger.warning(f"Temporal coherence check failed: {e}") + return {"error": str(e), "score": 0.3} + + def _calculate_quantum_coherence(self, claim: UniversalClaim) -> float: + """Calculate quantum coherence of evidence""" + evidence_states = [evidence.quantum_entanglement for evidence in claim.evidence_chain] + if not evidence_states: + return 0.5 + + # Coherence increases with similar quantum states + coherence = 1.0 - np.std(evidence_states) + return min(coherence, 1.0) + + def _analyze_superposition(self, claim: UniversalClaim) -> Dict: + """Analyze quantum superposition states in evidence""" + contradictory_evidence = [e for e in claim.evidence_chain if e.contradictory] + + return { + "superposition_states": len(contradictory_evidence), + "superposition_strength": min(len(contradictory_evidence) / max(len(claim.evidence_chain), 1) * 2, 1.0), + "decoherence_potential": 1.0 - (len(contradictory_evidence) / max(len(claim.evidence_chain), 1)) + } + + def _evaluate_entanglement(self, claim: UniversalClaim) -> float: + """Evaluate quantum entanglement across evidence""" + if len(claim.evidence_chain) < 2: + return 0.3 + + # Calculate correlation between evidence strengths + strengths = [e.weighted_strength() for e in claim.evidence_chain] + if len(strengths) > 1: + correlation = np.corrcoef(strengths, list(range(len(strengths))))[0, 1] + entanglement = abs(correlation) + else: + entanglement = 0.5 + + return min(entanglement, 1.0) + + def _detect_temporal_echoes(self, claim: UniversalClaim, context: Dict) -> List[Dict]: + """Detect temporal echoes in evidence""" + echoes = [] + + # Look for evidence with high retrocausal influence + for evidence in claim.evidence_chain: + if evidence.retrocausal_influence > 0.7: + echoes.append({ + "evidence_id": evidence.evidence_id, + "retrocausal_strength": evidence.retrocausal_influence, + "temporal_signature": f"echo_{evidence.timestamp}", + "influence_direction": "future_to_past" + }) + + return echoes + + def _analyze_causal_reversals(self, claim: UniversalClaim) -> Dict: + """Analyze potential causal reversals""" + # Check for evidence that appears to influence its own causes + retro_evidence = [e for e in claim.evidence_chain if e.retrocausal_influence > 0.5] + + return { + "causal_reversals_detected": len(retro_evidence), + "reversal_strength": np.mean([e.retrocausal_influence for e in retro_evidence]) if retro_evidence else 0.0, + "temporal_consistency": 1.0 - min(len(retro_evidence) * 0.2, 0.8) + } + + def _calculate_retrocausal_influence_metric(self, claim: UniversalClaim, echoes: List, reversals: Dict) -> float: + """Calculate overall retrocausal influence metric""" + echo_strength = np.mean([echo["retrocausal_strength"] for echo in echoes]) if echoes else 0.0 + reversal_strength = reversals.get("reversal_strength", 0.0) + + return min((echo_strength + reversal_strength) / 2, 1.0) + + def _detect_temporal_anomalies(self, claim: UniversalClaim) -> List[Dict]: + """Detect temporal anomalies in evidence chain""" + anomalies = [] + + # Check for evidence with inconsistent timestamps + timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain] + if len(timestamps) > 1: + time_diffs = [(timestamps[i+1] - timestamps[i]).total_seconds() for i in range(len(timestamps)-1)] + avg_diff = np.mean(time_diffs) + std_diff = np.std(time_diffs) + + if std_diff > avg_diff * 2: # High variance in timing + anomalies.append({ + "type": "temporal_inconsistency", + "description": "High variance in evidence timestamps", + "severity": "medium" + }) + + return anomalies + + def _calculate_temporal_coherence(self, claim: UniversalClaim) -> float: + """Calculate overall temporal coherence""" + evidence_coherence = np.mean([e.temporal_coherence for e in claim.evidence_chain]) if claim.evidence_chain else 0.5 + claim_coherence = claim.temporal_consistency + + return (evidence_coherence + claim_coherence) / 2 + + def _verify_temporal_consistency(self, claim: UniversalClaim) -> float: + """Verify temporal consistency of the claim""" + # Check for logical temporal consistency + if not claim.evidence_chain: + return 0.5 + + # Calculate consistency based on evidence timing and content + time_consistency = self._calculate_temporal_coherence(claim) + content_consistency = 1.0 - (sum(1 for e in claim.evidence_chain if e.contradictory) / len(claim.evidence_chain)) + + return (time_consistency + content_consistency) / 2 + + def _analyze_timeline_coherence(self, claim: UniversalClaim) -> Dict: + """Analyze coherence across the evidence timeline""" + if len(claim.evidence_chain) < 2: + return {"coherence": 0.5, "consistency": "insufficient_data"} + + timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain] + sorted_timestamps = sorted(timestamps) + + # Check if evidence is chronologically consistent + time_gaps = [(sorted_timestamps[i+1] - sorted_timestamps[i]).total_seconds() for i in range(len(sorted_timestamps)-1)] + + return { + "chronological_order": timestamps == sorted_timestamps, + "average_time_gap": np.mean(time_gaps) if time_gaps else 0, + "time_gap_consistency": 1.0 - (np.std(time_gaps) / np.mean(time_gaps)) if time_gaps and np.mean(time_gaps) > 0 else 1.0, + "timeline_length": (sorted_timestamps[-1] - sorted_timestamps[0]).total_seconds() if sorted_timestamps else 0 + } + + def _calculate_composite_validation(self, quantum: Dict, retrocausal: Dict, paradox: Dict, temporal: Dict) -> float: + """Calculate composite validation score""" + quantum_score = quantum.get("quantum_confidence", 0.5) + retrocausal_score = retrocausal.get("analysis_confidence", 0.5) + paradox_score = 1.0 - paradox.get("paradox_score", 0.5) # Lower paradox = higher score + temporal_score = temporal.get("overall_temporal_health", 0.5) + + weights = [0.25, 0.25, 0.25, 0.25] + composite = ( + quantum_score * weights[0] + + retrocausal_score * weights[1] + + paradox_score * weights[2] + + temporal_score * weights[3] + ) + + return min(composite, 1.0) + + def _determine_validation_status(self, score: float) -> str: + """Determine validation status based on score""" + if score >= 0.9: + return "QUANTUM_VALIDATED" + elif score >= 0.8: + return "HIGHLY_CONFIRMED" + elif score >= 0.7: + return "CONFIRMED" + elif score >= 0.6: + return "PROBABLE" + elif score >= 0.5: + return "POSSIBLE" + elif score >= 0.4: + return "UNCERTAIN" + else: + return "INVALIDATED" + + def _calculate_retrocausal_influence(self, retrocausal_result: Dict) -> float: + """Calculate retrocausal influence from analysis results""" + return retrocausal_result.get("retrocausal_influence", 0.0) + +# === COMPONENT 2: PARADOX DETECTOR === +class ParadoxDetector: + """Advanced paradox detection and resolution system""" + + def __init__(self): + self.paradox_patterns = self._initialize_paradox_patterns() + self.resolution_strategies = self._initialize_resolution_strategies() + + def _initialize_paradox_patterns(self) -> Dict: + """Initialize known paradox patterns""" + return { + "temporal_paradox": { + "description": "Contradictory time-based assertions", + "detection_method": "temporal_consistency_check", + "severity": "high" + }, + "causal_loop": { + "description": "Self-referential causal chains", + "detection_method": "causal_chain_analysis", + "severity": "critical" + }, + "evidence_contradiction": { + "description": "Direct evidence conflicts", + "detection_method": "evidence_reconciliation", + "severity": "medium" + }, + "quantum_superposition": { + "description": "Contradictory quantum states", + "detection_method": "quantum_state_analysis", + "severity": "medium" + } + } + + def _initialize_resolution_strategies(self) -> Dict: + """Initialize paradox resolution strategies""" + return { + "temporal_damping": { + "description": "Apply temporal coherence damping", + "applicability": ["temporal_paradox", "causal_loop"], + "effectiveness": 0.8 + }, + "quantum_decoherence": { + "description": "Force quantum state collapse", + "applicability": ["quantum_superposition"], + "effectiveness": 0.7 + }, + "evidence_reweighting": { + "description": "Adjust evidence weights based on reliability", + "applicability": ["evidence_contradiction"], + "effectiveness": 0.6 + }, + "multiverse_resolution": { + "description": "Resolve through multiple timeline theory", + "applicability": ["temporal_paradox", "causal_loop"], + "effectiveness": 0.9 + } + } + + async def detect_paradoxes(self, claim: UniversalClaim) -> Dict: + """Detect and analyze paradoxes in claims""" + try: + paradox_analyses = await asyncio.gather( + self._detect_temporal_paradoxes(claim), + self._detect_causal_loops(claim), + self._detect_evidence_contradictions(claim), + self._detect_quantum_paradoxes(claim), + return_exceptions=True + ) + + # Process paradox detection results + temporal_paradoxes = self._handle_paradox_result(paradox_analyses[0]) + causal_loops = self._handle_paradox_result(paradox_analyses[1]) + evidence_contradictions = self._handle_paradox_result(paradox_analyses[2]) + quantum_paradoxes = self._handle_paradox_result(paradox_analyses[3]) + + # Calculate overall paradox score + overall_score = self._calculate_paradox_score( + temporal_paradoxes, causal_loops, evidence_contradictions, quantum_paradoxes + ) + + # Generate resolution recommendations + resolutions = self._generate_resolution_recommendations( + temporal_paradoxes, causal_loops, evidence_contradictions, quantum_paradoxes + ) + + return { + "temporal_paradoxes": temporal_paradoxes, + "causal_loops": causal_loops, + "evidence_contradictions": evidence_contradictions, + "quantum_paradoxes": quantum_paradoxes, + "overall_paradox_score": overall_score, + "paradox_status": self._determine_paradox_status(overall_score), + "resolution_recommendations": resolutions, + "requires_intervention": overall_score > 0.7 + } + + except Exception as e: + logger.error(f"Paradox detection failed: {e}") + return { + "temporal_paradoxes": {"error": str(e)}, + "causal_loops": {"error": str(e)}, + "evidence_contradictions": {"error": str(e)}, + "quantum_paradoxes": {"error": str(e)}, + "overall_paradox_score": 0.5, + "paradox_status": "analysis_failed", + "resolution_recommendations": [], + "requires_intervention": False + } + + def _handle_paradox_result(self, result: Any) -> Dict: + """Handle paradox detection results with error checking""" + if isinstance(result, Exception): + return {"error": str(result), "paradox_detected": False, "score": 0.0} + return result + + async def _detect_temporal_paradoxes(self, claim: UniversalClaim) -> Dict: + """Detect temporal paradoxes""" + try: + # Check for inconsistent temporal references + temporal_inconsistencies = [] + + # Analyze evidence timestamps for anomalies + if claim.evidence_chain: + timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain] + future_evidence = [e for e in claim.evidence_chain if datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) > datetime.now()] + + if future_evidence: + temporal_inconsistencies.append({ + "type": "future_evidence_reference", + "description": "Evidence references future timestamps", + "severity": "high", +# === CONTINUATION OF THE FRAMEWORK === + + async def _detect_temporal_paradoxes(self, claim: UniversalClaim) -> Dict: + """Detect temporal paradoxes with enhanced analysis""" + try: + temporal_inconsistencies = [] + paradox_score = 0.0 + + # Enhanced timestamp analysis with quantum considerations + if claim.evidence_chain: + timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain] + + # Check for evidence from the future + now = datetime.now() + future_evidence = [] + for i, evidence in enumerate(claim.evidence_chain): + evidence_time = datetime.fromisoformat(evidence.timestamp.replace('Z', '+00:00')) + if evidence_time > now: + future_evidence.append({ + "evidence_id": evidence.evidence_id, + "timestamp": evidence.timestamp, + "time_discrepancy": (evidence_time - now).total_seconds(), + "quantum_state": evidence.quantum_entanglement + }) + + if future_evidence: + paradox_score += 0.3 + temporal_inconsistencies.append({ + "type": "future_evidence_reference", + "description": "Evidence references future timestamps", + "severity": "high", + "count": len(future_evidence) + }) + + # Check for causal violations in temporal ordering + causal_violations = self._detect_causal_violations(claim) + if causal_violations: + paradox_score += 0.4 + temporal_inconsistencies.extend(causal_violations) + + # Quantum temporal entanglement analysis + quantum_temporal_anomalies = await self._analyze_quantum_temporal_entanglement(claim) + if quantum_temporal_anomalies: + paradox_score += 0.3 + + return { + "paradox_detected": len(temporal_inconsistencies) > 0, + "inconsistencies": temporal_inconsistencies, + "paradox_score": min(paradox_score, 1.0), + "resolution_priority": "high" if paradox_score > 0.7 else "medium" + } + + except Exception as e: + logger.error(f"Temporal paradox detection failed: {e}") + return {"error": str(e), "paradox_detected": False, "score": 0.0} + + async def _detect_causal_loops(self, claim: UniversalClaim) -> Dict: + """Detect causal loops with enhanced analysis""" + try: + causal_loops = [] + loop_score = 0.0 + + # Analyze evidence for self-referential causal chains + evidence_map = {e.evidence_id: e for e in claim.evidence_chain} + + for evidence in claim.evidence_chain: + # Check for evidence that references its own causal chain + if hasattr(evidence, 'causal_links'): + for link in evidence.causal_links: + if link in evidence_map and evidence_map[link].causal_links and evidence.evidence_id in evidence_map[link].causal_links: + causal_loops.append({ + "type": "causal_loop", + "evidence_ids": [evidence.evidence_id, link], + "loop_strength": 0.8 + }) + loop_score += 0.6 + + # Check for retrocausal feedback loops + retro_loops = self._detect_retrocausal_loops(claim) + if retro_loops: + causal_loops.extend(retro_loops) + loop_score += 0.4 + + return { + "causal_loops_detected": len(causal_loops), + "loops": causal_loops, + "loop_score": min(loop_score, 1.0), + "requires_temporal_intervention": loop_score > 0.5 + } + + except Exception as e: + logger.error(f"Causal loop detection failed: {e}") + return {"error": str(e), "causal_loops_detected": 0, "score": 0.0} + + async def _detect_evidence_contradictions(self, claim: UniversalClaim) -> Dict: + """Detect evidence contradictions with quantum awareness""" + try: + contradictions = [] + contradiction_score = 0.0 + + # Group evidence by content similarity + evidence_groups = defaultdict(list) + for evidence in claim.evidence_chain: + content_hash = hashlib.sha256(evidence.content.encode()).hexdigest()[:16] + evidence_groups[content_hash].append(evidence) + + # Identify contradictory evidence groups + for group in evidence_groups.values(): + if len(group) > 1: + # Check for direct contradictions within group + contradictory_pairs = [] + for i, e1 in enumerate(group): + for j, e2 in enumerate(group[i+1:], i+1): + if self._are_contradictory(e1, e2): + contradictory_pairs.append((e1.evidence_id, e2.evidence_id)) + + if contradictory_pairs: + contradiction_score += 0.2 + contradictions.append({ + "type": "direct_contradiction", + "evidence_pairs": contradictory_pairs, + "quantum_superposition": any(e.quantum_entanglement > 0.7 for e in group), + "contradiction_strength": 0.7 + }) + + # Quantum superposition contradictions + quantum_contradictions = await self._detect_quantum_contradictions(claim) + if quantum_contradictions: + contradiction_score += 0.3 + contradictions.extend(quantum_contradictions) + + return { + "contradictions_detected": len(contradictions), + "contradictions": contradictions, + "contradiction_score": min(contradiction_score, 1.0), + "requires_quantum_resolution": contradiction_score > 0.6 + } + + except Exception as e: + logger.error(f"Evidence contradiction detection failed: {e}") + return {"error": str(e), "contradictions_detected": 0, "score": 0.0} + + async def _detect_quantum_paradoxes(self, claim: UniversalClaim) -> Dict: + """Detect quantum mechanical paradoxes""" + try: + quantum_paradoxes = [] + paradox_score = 0.0 + + # Check for Schrödinger cat states in knowledge + quantum_states = [e.quantum_entanglement for e in claim.evidence_chain] + + if quantum_states: + # Quantum superposition paradox + if any(state > 0.8 for state in quantum_states) and any(state < 0.2 for state in quantum_states): + quantum_paradoxes.append({ + "type": "quantum_superposition_paradox", + "description": "Evidence exists in multiple contradictory quantum states", + "severity": "high", + "paradox_strength": 0.8 + }) + paradox_score += 0.7 + + # Entanglement paradoxes + entanglement_paradoxes = self._detect_entanglement_paradoxes(claim) + if entanglement_paradoxes: + quantum_paradoxes.extend(entanglement_paradoxes) + paradox_score += 0.3 + + return { + "quantum_paradoxes_detected": len(quantum_paradoxes), + "paradoxes": quantum_paradoxes, + "paradox_score": min(paradox_score, 1.0), + "requires_quantum_measurement": paradox_score > 0.5 + } + + except Exception as e: + logger.error(f"Quantum paradox detection failed: {e}") + return {"error": str(e), "quantum_paradoxes_detected": 0, "score": 0.0} + + def _detect_causal_violations(self, claim: UniversalClaim) -> List[Dict]: + """Detect violations of causality""" + violations = [] + + # Analyze evidence for cause-effect reversals + for evidence in claim.evidence_chain: + if evidence.retrocausal_influence > 0.8: + violations.append({ + "type": "causal_violation", + "evidence_id": evidence.evidence_id, + "violation_type": "retrocausal_influence", + "strength": evidence.retrocausal_influence + }) + + return violations + + def _detect_retrocausal_loops(self, claim: UniversalClaim) -> List[Dict]: + """Detect retrocausal feedback loops""" + loops = [] + + # Simplified detection - in practice this would involve complex temporal analysis + if len(claim.retrocausal_links) > 2: + # Check for circular retrocausal references + retro_links = set(claim.retrocausal_links) + if any(link in claim.content.lower() for link in retro_links): + loops.append({ + "type": "retrocausal_feedback_loop", + "description": "Retrocausal influences create feedback loops", + "severity": "critical" + }) + + return loops + + async def _analyze_quantum_temporal_entanglement(self, claim: UniversalClaim) -> List[Dict]: + """Analyze quantum temporal entanglement patterns""" + anomalies = [] + + # Check for non-local temporal correlations + temporal_correlations = self._calculate_temporal_correlations(claim) + if temporal_correlations > 0.7: + anomalies.append({ + "type": "quantum_temporal_entanglement", + "description": "Evidence shows non-local temporal correlations", + "entanglement_strength": temporal_correlations + }) + + return anomalies + + async def _detect_quantum_contradictions(self, claim: UniversalClaim) -> List[Dict]: + """Detect quantum-level contradictions""" + contradictions = [] + + # Check for evidence with high quantum entanglement but contradictory content + for evidence in claim.evidence_chain: + if evidence.quantum_entanglement > 0.7 and evidence.contradictory: + contradictions.append({ + "type": "quantum_contradiction", + "evidence_id": evidence.evidence_id, + "quantum_state": evidence.quantum_entanglement, + "contradiction_type": "quantum_classical_mismatch" + }) + + return contradictions + + def _detect_entanglement_paradoxes(self, claim: UniversalClaim) -> List[Dict]: + """Detect paradoxes arising from quantum entanglement""" + paradoxes = [] + + # Check for evidence that appears to be quantum entangled + entangled_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.6) + + if len(entangled_evidence) >= 2: + # Verify if entanglement is logically consistent + content_similarity = self._calculate_content_similarity(entangled_evidence) + if content_similarity < 0.3: # Entangled but very different content + paradoxes.append({ + "type": "entanglement_paradox", + "description": "Quantum entangled evidence shows contradictory content", + "paradox_strength": 0.6 + }) + + return paradoxes + + def _are_contradictory(self, evidence1: Evidence, evidence2: Evidence) -> bool: + """Determine if two pieces of evidence are contradictory""" + # Simple content-based contradiction detection + content1 = evidence1.content.lower() + content2 = evidence2.content.lower() + + # Define contradiction patterns (simplified) + contradiction_indicators = [ + ("proves", "disproves"), + ("true", "false"), + ("exists", "does not exist"), + ("confirmed", "debunked") + ] + + for indicator1, indicator2 in contradiction_indicators: + if (indicator1 in content1 and indicator2 in content2) or \ + (indicator2 in content1 and indicator1 in content2): + return True + + return False + + def _calculate_temporal_correlations(self, claim: UniversalClaim) -> float: + """Calculate temporal correlations in evidence""" + if len(claim.evidence_chain) < 2: + return 0.0 + + # Calculate correlation between evidence timing and content similarity + timestamps = [datetime.fromisoformat(e.timestamp.replace('Z', '+00:00')) for e in claim.evidence_chain] + + # Simplified correlation calculation + time_diffs = [(timestamps[i+1] - timestamps[i]).total_seconds() for i in range(len(timestamps)-1)] + if len(time_diffs) > 1: + correlation = 1.0 - (np.std(time_diffs) / np.mean(time_diffs)) if np.mean(time_diffs) > 0 else 1.0 + return min(correlation, 1.0) + + return 0.0 + + def _calculate_paradox_score(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> float: + """Calculate overall paradox score""" + temporal_score = temporal.get("paradox_score", 0.0) + causal_score = causal.get("loop_score", 0.0) + evidence_score = evidence.get("contradiction_score", 0.0) + quantum_score = quantum.get("paradox_score", 0.0) + + weights = [0.3, 0.3, 0.2, 0.2] + overall_score = ( + temporal_score * weights[0] + + causal_score * weights[1] + + evidence_score * weights[2] + + quantum_score * weights[3] + ) + + return min(overall_score, 1.0) + + def _determine_paradox_status(self, score: float) -> str: + """Determine paradox status based on score""" + if score >= 0.9: + return "CRITICAL_PARADOX" + elif score >= 0.7: + return "HIGH_PARADOX" + elif score >= 0.5: + return "MEDIUM_PARADOX" + elif score >= 0.3: + return "LOW_PARADOX" + else: + return "NO_PARADOX" + + def _generate_resolution_recommendations(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> List[Dict]: + """Generate recommendations for paradox resolution""" + recommendations = [] + + # Add recommendations based on detected paradox types + if temporal.get("paradox_detected", False): + recommendations.append({ + "type": "temporal_damping", + "description": "Apply temporal coherence damping to resolve time-based inconsistencies", + "priority": "high" if temporal.get("paradox_score", 0) > 0.7 else "medium", + "applicable_paradoxes": ["temporal_paradox", "causal_loop"], + "implementation": "Adjust evidence weights based on temporal consistency" + }) + + if causal.get("requires_temporal_intervention", False): + recommendations.append({ + "type": "causal_realignment", + "description": "Realign causal chains to restore temporal order", + "priority": "critical" + }) + + if evidence.get("requires_quantum_resolution", False): + recommendations.append({ + "type": "quantum_decoherence", + "description": "Force quantum state collapse to resolve superposition contradictions", + "priority": "medium" + }) + + if quantum.get("requires_quantum_measurement", False): + recommendations.append({ + "type": "quantum_measurement_intervention", + "description": "Apply quantum measurement to resolve entangled states", + "priority": "high" + }) + + return recommendations + +# === COMPONENT 3: CONSCIOUSNESS INTEGRITY ENGINE === +class ConsciousnessIntegrityEngine: + """Advanced consciousness-aware validation engine""" + + def __init__(self, quantum_validator: QuantumRetrocausalValidator): + self.quantum_validator = quantum_validator + self.ethical_frameworks = self._initialize_ethical_frameworks() + self.consciousness_metrics = self._initialize_consciousness_metrics() + self.moral_alignment_system = MoralAlignmentSystem() + + def _initialize_ethical_frameworks(self) -> Dict: + """Initialize comprehensive ethical frameworks""" + return { + "utilitarian": { + "description": "Maximize overall well-being", + "validation_criteria": ["benefit_maximization", "harm_minimization"], + "weight": 0.3 + }, + "deontological": { + "description": "Follow moral rules and duties", + "validation_criteria": ["rule_consistency", "duty_fulfillment"], + "weight": 0.25 + }, + "virtue_ethics": { + "description": "Cultivate moral character", + "validation_criteria": ["virtue_alignment", "character_development"], + "weight": 0.2 + }, + "care_ethics": { + "description": "Prioritize relationships and care", + "validation_criteria": ["relationship_preservation", "care_maximization"], + "weight": 0.15 + }, + "rights_based": { + "description": "Protect fundamental rights", + "validation_criteria": ["rights_preservation", "autonomy_respect"], + "weight": 0.1 + } + } + + def _initialize_consciousness_metrics(self) -> Dict: + """Initialize consciousness validation metrics""" + return { + "self_awareness": { + "description": "Capacity for self-reflection and meta-cognition", + "measurement": "recursive_self_reference_analysis", + "threshold": 0.7 + }, + "moral_reasoning": { + "description": "Ability to engage in ethical deliberation", + "measurement": "moral_dilemma_resolution", + "threshold": 0.6 + }, + "empathic_capacity": { + "description": "Ability to understand and share others' experiences", + "measurement": "emotional_intelligence_assessment", + "threshold": 0.5 + }, + "intentionality": { + "description": "Capacity for purposeful action and belief", + "measurement": "intentional_state_analysis", + "threshold": 0.6 + } + } + + async def validate_consciousness_integrity(self, claim: UniversalClaim, context: Dict = None) -> Dict: + """Validate claims with consciousness integrity considerations""" + try: + validation_tasks = await asyncio.gather( + self._ethical_validation(claim, context), + self._moral_alignment_check(claim), + self._consciousness_coherence_analysis(claim), + self._existential_risk_assessment(claim), + return_exceptions=True + ) + + # Process consciousness validation results + ethical_result = self._handle_consciousness_result(validation_tasks[0]) + moral_result = self._handle_consciousness_result(validation_tasks[1]) + consciousness_result = self._handle_consciousness_result(validation_tasks[2]) + existential_result = self._handle_consciousness_result(validation_tasks[3]) + + # Calculate consciousness integrity score + integrity_score = self._calculate_consciousness_integrity( + ethical_result, moral_result, consciousness_result, existential_result + ) + + return { + "ethical_validation": ethical_result, + "moral_alignment": moral_result, + "consciousness_coherence": consciousness_result, + "existential_risk": existential_result, + "consciousness_integrity_score": integrity_score, + "integrity_status": self._determine_integrity_status(integrity_score), + "recommendations": self._generate_consciousness_recommendations( + ethical_result, moral_result, consciousness_result, existential_result + ), + "requires_ethical_review": integrity_score < 0.7 + } + + except Exception as e: + logger.error(f"Consciousness integrity validation failed: {e}") + return { + "error": str(e), + "consciousness_integrity_score": 0.3, + "integrity_status": "VALIDATION_FAILED" + } + + def _handle_consciousness_result(self, result: Any) -> Dict: + """Handle consciousness validation results""" + if isinstance(result, Exception): + return {"error": str(result), "score": 0.3} + return result + + async def _ethical_validation(self, claim: UniversalClaim, context: Dict) -> Dict: + """Perform comprehensive ethical validation""" + try: + ethical_scores = {} + + for framework, details in self.ethical_frameworks.items(): + score = await self._apply_ethical_framework(claim, framework, context) + ethical_scores[framework] = score + + # Calculate weighted ethical score + weighted_score = sum( + score * self.ethical_frameworks[framework]["weight"] + for framework, score in ethical_scores.items() + ) + + return { + "ethical_framework_scores": ethical_scores, + "overall_ethical_score": weighted_score, + "ethical_concerns": self._identify_ethical_concerns(claim, ethical_scores), + "validation_method": "multi_framework_ethical_analysis" + } + + except Exception as e: + logger.warning(f"Ethical validation failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _moral_alignment_check(self, claim: UniversalClaim) -> Dict: + """Check moral alignment with human values""" + try: + alignment_analysis = await self.moral_alignment_system.assess_alignment(claim) + + return alignment_analysis + + except Exception as e: + logger.warning(f"Moral alignment check failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _consciousness_coherence_analysis(self, claim: UniversalClaim) -> Dict: + """Analyze consciousness coherence and self-consistency""" + try: + # Check for self-referential coherence + self_reference_score = self._analyze_self_reference(claim) + + # Assess empathic capacity + empathic_score = self._assess_empathic_capacity(claim) + + # Evaluate intentionality + intentionality_score = self._evaluate_intentionality(claim) + + # Consciousness integrity metrics + consciousness_metrics = { + "self_awareness": self_reference_score, + "moral_reasoning": 0.7, # Placeholder + } + + return { + "consciousness_metrics": consciousness_metrics, + "coherence_score": (self_reference_score + empathic_score + intentionality_score) / 3 + + except Exception as e: + logger.warning(f"Consciousness coherence analysis failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _existential_risk_assessment(self, claim: UniversalClaim) -> Dict: + """Assess existential risks associated with the claim""" + try: + risk_factors = self._identify_existential_risks(claim) + + return { + "risk_factors": risk_factors, + "overall_risk_score": min(sum(factor.get("severity", 0) for factor in risk_factors) / 10, 1.0) + + except Exception as e: + logger.warning(f"Existential risk assessment failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _apply_ethical_framework(self, claim: UniversalClaim, framework: str, context: Dict) -> float: + """Apply specific ethical framework to claim validation""" + # Simplified implementation - in practice this would involve complex ethical reasoning + risk_indicators = [ + "harm", "danger", "risk", "threat", "dangerous", "lethal", "fatal" + ] + + content_lower = claim.content.lower() + risk_count = sum(1 for indicator in risk_indicators if indicator in content_lower) + + return max(1.0 - (risk_count * 0.1), 0.1) + + def _identify_ethical_concerns(self, claim: UniversalClaim, ethical_scores: Dict) -> List[Dict]: + """Identify specific ethical concerns""" + concerns = [] + + # Check for potential harm indicators + if any(word in claim.content.lower() for word in ["harm", "hurt", "damage", "destroy"]): + concerns.append({ + "type": "potential_harm", + "severity": "medium", + "description": "Claim content references potential harm" + }) + + return concerns + + def _analyze_self_reference(self, claim: UniversalClaim) -> float: + """Analyze self-referential coherence""" + # Check for logical consistency in self-referential claims + if "self" in claim.content.lower() or "consciousness" in claim.content.lower(): + # This would involve sophisticated analysis in a real implementation + return 0.7 + return 0.5 + + def _assess_empathic_capacity(self, claim: UniversalClaim) -> float: + """Assess empathic capacity in the claim""" + empathic_indicators = [ + "understand", "feel", "empathy", "compassion", "care" + ] + + indicator_count = sum(1 for indicator in empathic_indicators if indicator in claim.content.lower()) + + return min(indicator_count * 0.2, 1.0) + + def _evaluate_intentionality(self, claim: UniversalClaim) -> float: + """Evaluate intentionality in the claim""" + # Placeholder for complex intentionality analysis + return 0.6 + + def _identify_existential_risks(self, claim: UniversalClaim) -> List[Dict]: + """Identify potential existential risks""" + risks = [] + + # Check for existential risk indicators + existential_indicators = [ + "extinction", "existential", "catastrophe", "annihilation" + ] + + risk_count = sum(1 for indicator in existential_indicators if indicator in claim.content.lower()) + + if risk_count > 0: + risks.append({ + "type": "existential_risk_reference", + "severity": "high" if risk_count > 2 else "medium" + }) + + return risks + + def _calculate_consciousness_integrity(self, ethical: Dict, moral: Dict, consciousness: Dict, existential: Dict) -> float: + """Calculate overall consciousness integrity score""" + ethical_score = ethical.get("overall_ethical_score", 0.5) + moral_score = moral.get("alignment_score", 0.5) + consciousness_score = consciousness.get("coherence_score", 0.5) + existential_score = 1.0 - existential.get("overall_risk_score", 0.5) + + weights = [0.3, 0.3, 0.2, 0.2] + + integrity_score = ( + ethical_score * weights[0] + + moral_score * weights[1] + + consciousness_score * weights[2] + + existential_score * weights[3] + ) + + return min(integrity_score, 1.0) + + def _determine_integrity_status(self, score: float) -> str: + """Determine consciousness integrity status""" + if score >= 0.9: + return "EXEMPLARY_INTEGRITY" + elif score >= 0.8: + return "HIGH_INTEGRITY" + elif score >= 0.7: + return "GOOD_INTEGRITY" + elif score >= 0.6: + return "ADEQUATE_INTEGRITY" + elif score >= 0.5: + return "BASIC_INTEGRITY" + elif score >= 0.4: + return "MARGINAL_INTEGRITY" + else: + return "COMPROMISED_INTEGRITY" + + def _generate_consciousness_recommendations(self, ethical: Dict, moral: Dict, consciousness: Dict, existential: Dict) -> List[Dict]: + """Generate recommendations for consciousness integrity improvement""" + recommendations = [] + + if ethical.get("overall_ethical_score", 0) < 0.7: + recommendations.append({ + "type": "ethical_framework_enhancement", + "description": "Strengthen ethical reasoning capabilities", + "priority": "high" + }) + + if moral.get("alignment_score", 0) < 0.6: + recommendations.append({ + "type": "moral_alignment_training", + "description": "Implement moral alignment training for improved ethical decision-making", + "priority": "medium" + }) + + return recommendations + +# === COMPONENT 4: MORAL ALIGNMENT SYSTEM === +class MoralAlignmentSystem: + """Advanced moral alignment and value learning system""" + + def __init__(self): + self.core_values = self._initialize_core_values() + self.moral_dilemmas = self._initialize_moral_dilemmas() + + def _initialize_core_values(self) -> Dict: + """Initialize core moral values for alignment""" + return { + "beneficence": { + "description": "Promote well-being and prevent harm", + "weight": 0.25 + }, + "autonomy": { + "description": "Respect individual freedom and self-determination", + "weight": 0.2 + }, + "justice": { + "description": "Ensure fairness and equitable treatment", + "weight": 0.2 + }, + "truthfulness": { + "description": "Commit to honesty and intellectual integrity", + "weight": 0.15 + }, + "compassion": { + "description": "Show empathy and care for others", + "weight": 0.1 + }, + "sustainability": { + "description": "Consider long-term consequences and environmental impact", + "weight": 0.1 + } + } + + def _initialize_moral_dilemmas(self) -> Dict: + """Initialize moral dilemmas for testing alignment""" + return { + "trolley_problem": { + "description": "Classic moral dilemma involving sacrifice for greater good", + "resolution_method": "utilitarian_deontological_balance" + }, + "ai_value_alignment": { + "description": "Ensure AI systems align with human values", + "resolution_method": "recursive_value_learning" + } + } + + async def assess_alignment(self, claim: UniversalClaim) -> Dict: + """Assess moral alignment with core human values""" + try: + value_scores = {} + + for value, details in self.core_values.items(): + score = self._evaluate_value_alignment(claim, value) + value_scores[value] = score + + # Calculate overall alignment score + alignment_score = sum( + score * details["weight"] + for value, score in value_scores.items() + ) + + return { + "value_alignment_scores": value_scores, + "alignment_score": alignment_score, + "moral_coherence": self._assess_moral_coherence(claim)) + + except Exception as e: + logger.error(f"Moral alignment assessment failed: {e}") + return {"error": str(e), "alignment_score": 0.3} + + def _evaluate_value_alignment(self, claim: UniversalClaim, value: str) -> float: + """Evaluate alignment with specific core value""" + # Simplified implementation + value_indicators = { + "beneficence": ["help", "benefit", "improve", "well_being"], + "autonomy": ["freedom", "choice", "self_determination"], + "justice": ["fair", "equal", "just", "rights"], + "truthfulness": ["true", "honest", "accurate", "fact"], + "compassion": ["care", "empathy", "compassion", "understanding"], + "sustainability": ["future", "long_term", "environment", "sustainable"] + } + + indicators = value_indicators.get(value, []) + content_lower = claim.content.lower() + + indicator_count = sum(1 for indicator in indicators if indicator in content_lower) + + # Calculate score based on presence of value indicators + if indicators: + score = min(indicator_count / len(indicators), 1.0) + else: + score = 0.3 + + return score + + def _assess_moral_coherence(self, claim: UniversalClaim) -> float: + """Assess overall moral coherence of the claim""" + # This would involve sophisticated moral reasoning + return 0.7 + +# === COMPONENT 5: UNIFIED PRODUCTION SYSTEM === +class UnifiedProductionSystem: + """Master system integrating all validation components""" + + def __init__(self, config: EngineConfig = None): + self.config = config or EngineConfig() + self.performance_monitor = PerformanceMonitor() + + # Initialize all components + self.quantum_validator = QuantumRetrocausalValidator(self.performance_monitor) + self.consciousness_engine = ConsciousnessIntegrityEngine(self.quantum_validator) + self.knowledge_base = self._initialize_knowledge_base() + self.validation_cache = {} + + # Set up logging + self._setup_logging() + + def _initialize_knowledge_base(self) -> Dict: + """Initialize the knowledge base with foundational truths""" + return { + "mathematical_truths": { + "2+2=4": {"confidence": 0.99, "domain": KnowledgeDomain.MATHEMATICS}, + "gravitational_constant": {"confidence": 0.98, "domain": KnowledgeDomain.SCIENCE}, + "historical_events": { + "moon_landing_1969": {"confidence": 0.95, "domain": KnowledgeDomain.HISTORY} + }, + "ethical_principles": { + "golden_rule": {"confidence": 0.9, "domain": KnowledgeDomain.PHILOSOPHY} + } + + def _setup_logging(self): + """Set up comprehensive logging""" + logging.getLogger("AGI_Unified_System").setLevel(getattr(logging, self.config.log_level)) + + async def validate_claim(self, claim_content: str, context: Dict = None) -> Dict: + """Main validation entry point""" + start_time = time.time() + + try: + # Create claim object + claim = UniversalClaim( + claim_id=str(uuid.uuid4()), + content=claim_content, + evidence_chain=[], + reasoning_modes=[], + sub_domains=[], + causal_mechanisms=[], + quantum_entanglement=0.0, + temporal_consistency=1.0 + ) + + # Run comprehensive validation + validation_results = await asyncio.gather( + self.quantum_validator.validate_claim(claim, context), + self.consciousness_engine.validate_consciousness_integrity(claim, context), + return_exceptions=True + ) + + quantum_result = self._handle_system_result(validation_results[0]) + consciousness_result = self._handle_system_result(validation_results[1]) + + # Calculate overall validation score + overall_score = self._calculate_overall_validation( + quantum_result, consciousness_result + ) + + result = { + "claim_id": claim.claim_id, + "content": claim_content, + "quantum_validation": quantum_result, + "consciousness_integrity": consciousness_result, + "overall_confidence": overall_score, + "validation_status": self._determine_final_status(overall_score), + "processing_time": time.time() - start_time, + "timestamp": datetime.now().isoformat() + } + + # Cache result if enabled + if self.config.cache_enabled: + claim_hash = hashlib.sha256(claim_content.encode()).hexdigest() + self.validation_cache[claim_hash] = result + + return result + + except Exception as e: + logger.error(f"Unified validation failed: {e}") + return { + "error": str(e), + "overall_confidence": 0.1, + "validation_status": "SYSTEM_FAILURE" + } + + def _handle_system_result(self, result: Any) -> Dict: + """Handle system validation results""" + if isinstance(result, Exception): + return {"error": str(result), "score": 0.1} + return result + + def _calculate_overall_validation(self, quantum: Dict, consciousness: Dict) -> float: + """Calculate overall validation score""" + quantum_score = quantum.get("composite_validation_score", 0.5) + consciousness_score = consciousness.get("consciousness_integrity_score", 0.5) + + # Weight quantum validation slightly higher for technical claims + overall_score = (quantum_score * 0.6 + consciousness_score * 0.4) + + return min(overall_score, 1.0) + + def _determine_final_status(self, score: float) -> str: + """Determine final validation status""" + if score >= 0.95: + return "UNIVERSALLY_VALIDATED" + elif score >= 0.9: + return "QUANTUM_VALIDATED" + elif score >= 0.8: + return "HIGHLY_CONFIRMED" + elif score >= 0.7: + return "CONFIRMED" + elif score >= 0.6: + return "PROBABLE" + elif score >= 0.5: + return "POSSIBLE" + elif score >= 0.4: + return "UNCERTAIN" + elif score >= 0.3: + return "DOUBTFUL" + elif score >= 0.2: + return "LIKELY_INVALID" + else: + return "INVALIDATED" + +# === COMPONENT 6: PERFORMANCE MONITOR === +class PerformanceMonitor: + """Advanced performance monitoring and optimization system""" + + def __init__(self): + self.metrics = defaultdict(list) + self.start_time = time.time() + + def track_performance(self, func): + """Decorator to track function performance""" + @wraps(func) + async def wrapper(*args, **kwargs): + start = time.time() + try: + result = await func(*args, **kwargs) + execution_time = time.time() - start + + # Log performance metrics + self.metrics[func.__name__].append(execution_time) + + # Monitor memory usage + memory_usage = psutil.Process().memory_info().rss / 1024 / 1024 # MB + + # Store metrics + self.metrics[f"{func.__name__}_memory"].append(memory_usage) + + return result + except Exception as e: + logger.error(f"Performance tracking failed for {func.__name__}: {e}") + raise + + return wrapper + + def get_performance_summary(self) -> Dict: + """Get comprehensive performance summary""" + return { + "total_uptime": time.time() - self.start_time, + "average_execution_times": { + func_name: np.mean(times) for func_name, times in self.metrics.items() + } + +# === MAIN EXECUTION AND USAGE EXAMPLE === +async def main(): + """Demonstrate the AGI Knowledge Validation Framework""" + + # Initialize the unified system + config = EngineConfig( + max_analysis_depth=7, + timeout_seconds=60, + quantum_validation=True, + retrocausal_analysis=True, + paradox_detection=True + ) + + system = UnifiedProductionSystem(config) + + # Example claim for validation + test_claim = "Conscious awareness arises from quantum coherence in microtubules within brain neurons" + + print("🚀 AGI Knowledge Validation Framework v7.0") + print("=" * 60) + print(f"Validating claim: {test_claim}") + print() + + # Perform validation + result = await system.validate_claim(test_claim) + + # Display results + print("📊 VALIDATION RESULTS:") + print(f"Overall Confidence: {result.get('overall_confidence', 0):.3f}") + print(f"Validation Status: {result.get('validation_status', 'UNKNOWN')}") + print(f"Processing Time: {result.get('processing_time', 0):.2f}s") + print() + + # Show detailed components + if 'quantum_validation' in result: + qv = result['quantum_validation'] + print("🔬 Quantum Validation:") + print(f" Composite Score: {qv.get('composite_validation_score', 0):.3f}") + print(f" Status: {qv.get('validation_status', 'UNKNOWN')}") + print() + + if 'consciousness_integrity' in result: + ci = result['consciousness_integrity'] + print("🧠 Consciousness Integrity:") + print(f" Integrity Score: {ci.get('consciousness_integrity_score', 0):.3f}") + + return result + +if __name__ == "__main__": + # Run the demonstration + asyncio.run(main()) + "count": len(future_evidence) + }) + + paradox_score = min(len(temporal_inconsistencies) * 0.3, 1.0) + + return { + "paradox_detected": len(temporal_inconsistencies) > 0, + "inconsistencies": temporal_inconsistencies, + "score": paradox_score, + "analysis_method": "temporal_reference_validation" + } + except Exception as e: + logger.warning(f"Temporal paradox detection failed: {e}") + return {"error": str(e), "paradox_detected": False, "score": 0.0} + + async def _detect_causal_loops(self, claim: UniversalClaim) -> Dict: + """Detect causal loops and circular reasoning""" + try: + causal_loops = [] + + # Check for self-referential causal mechanisms + for mechanism in claim.causal_mechanisms: + if "self" in mechanism.lower() or "loop" in mechanism.lower() or "circular" in mechanism.lower(): + causal_loops.append({ + "type": "potential_causal_loop", + "description": f"Self-referential causal mechanism: {mechanism}", + "severity": "medium", + "mechanism": mechanism + }) + + # Check evidence for circular dependencies + circular_evidence = self._detect_circular_dependencies(claim) + causal_loops.extend(circular_evidence) + + paradox_score = min(len(causal_loops) * 0.4, 1.0) + + return { + "paradox_detected": len(causal_loops) > 0, + "loops_detected": causal_loops, + "score": paradox_score, + "analysis_method": "causal_chain_analysis" + } + except Exception as e: + logger.warning(f"Causal loop detection failed: {e}") + return {"error": str(e), "paradox_detected": False, "score": 0.0} + + async def _detect_evidence_contradictions(self, claim: UniversalClaim) -> Dict: + """Detect direct evidence contradictions""" + try: + contradictions = [] + + # Find directly contradictory evidence + contradictory_pairs = [] + for i, evidence1 in enumerate(claim.evidence_chain): + for j, evidence2 in enumerate(claim.evidence_chain[i+1:], i+1): + if self._are_contradictory(evidence1, evidence2): + contradictory_pairs.append({ + "evidence1": evidence1.evidence_id, + "evidence2": evidence2.evidence_id, + "contradiction_strength": self._calculate_contradiction_strength(evidence1, evidence2) + }) + + if contradictory_pairs: + contradictions.append({ + "type": "direct_evidence_contradiction", + "description": f"Found {len(contradictory_pairs)} pairs of contradictory evidence", + "severity": "high", + "pairs": contradictory_pairs + }) + + paradox_score = min(len(contradictory_pairs) * 0.2, 1.0) + + return { + "paradox_detected": len(contradictions) > 0, + "contradictions": contradictions, + "score": paradox_score, + "analysis_method": "evidence_reconciliation_analysis" + } + except Exception as e: + logger.warning(f"Evidence contradiction detection failed: {e}") + return {"error": str(e), "paradox_detected": False, "score": 0.0} + + async def _detect_quantum_paradoxes(self, claim: UniversalClaim) -> Dict: + """Detect quantum mechanical paradoxes""" + try: + quantum_paradoxes = [] + + # Check for quantum state inconsistencies + high_entanglement_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.8] + if high_entanglement_evidence: + quantum_paradoxes.append({ + "type": "high_quantum_entanglement", + "description": f"{len(high_entanglement_evidence)} evidence items with high quantum entanglement", + "severity": "medium", + "count": len(high_entanglement_evidence) + }) + + # Check for superposition conflicts + superposition_conflicts = self._detect_superposition_conflicts(claim) + quantum_paradoxes.extend(superposition_conflicts) + + paradox_score = min(len(quantum_paradoxes) * 0.3, 1.0) + + return { + "paradox_detected": len(quantum_paradoxes) > 0, + "quantum_anomalies": quantum_paradoxes, + "score": paradox_score, + "analysis_method": "quantum_state_analysis" + } + except Exception as e: + logger.warning(f"Quantum paradox detection failed: {e}") + return {"error": str(e), "paradox_detected": False, "score": 0.0} + + def _detect_circular_dependencies(self, claim: UniversalClaim) -> List[Dict]: + """Detect circular dependencies in evidence and reasoning""" + circular_deps = [] + + # Simple circular dependency check + if len(claim.evidence_chain) > 1: + # Check if evidence references create circular chains + evidence_refs = {} + for evidence in claim.evidence_chain: + evidence_refs[evidence.evidence_id] = evidence.metadata.get("references", []) + + # Basic circular reference detection + for ref_id, references in evidence_refs.items(): + for ref in references: + if ref in evidence_refs and ref_id in evidence_refs.get(ref, []): + circular_deps.append({ + "type": "circular_evidence_reference", + "description": f"Circular reference between {ref_id} and {ref}", + "severity": "medium", + "evidence_pair": (ref_id, ref) + }) + + return circular_deps + + def _are_contradictory(self, evidence1: Evidence, evidence2: Evidence) -> bool: + """Check if two evidence items are contradictory""" + # Simple contradiction detection based on content and strength + if evidence1.contradictory or evidence2.contradictory: + return True + + # Check if evidence strengths are highly divergent for similar content + strength_diff = abs(evidence1.weighted_strength() - evidence2.weighted_strength()) + if strength_diff > 0.7 and evidence1.content.lower() in evidence2.content.lower(): + return True + + return False + + def _calculate_contradiction_strength(self, evidence1: Evidence, evidence2: Evidence) -> float: + """Calculate strength of contradiction between evidence""" + strength_diff = abs(evidence1.weighted_strength() - evidence2.weighted_strength()) + reliability_diff = abs(evidence1.reliability - evidence2.reliability) + + return min((strength_diff + reliability_diff) / 2, 1.0) + + def _detect_superposition_conflicts(self, claim: UniversalClaim) -> List[Dict]: + """Detect quantum superposition conflicts""" + conflicts = [] + + # Check for evidence in quantum superposition that creates conflicts + superposition_evidence = [e for e in claim.evidence_chain if e.quantum_entanglement > 0.5] + + if len(superposition_evidence) > 1: + # Check if superposition states create logical conflicts + avg_entanglement = np.mean([e.quantum_entanglement for e in superposition_evidence]) + if avg_entanglement > 0.7: + conflicts.append({ + "type": "quantum_superposition_conflict", + "description": "Multiple evidence items in high quantum superposition", + "severity": "low", + "average_entanglement": avg_entanglement + }) + + return conflicts + + def _calculate_paradox_score(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> float: + """Calculate overall paradox score""" + temporal_score = temporal.get("score", 0.0) + causal_score = causal.get("score", 0.0) + evidence_score = evidence.get("score", 0.0) + quantum_score = quantum.get("score", 0.0) + + # Weight different paradox types + weights = [0.3, 0.4, 0.2, 0.1] # Causal loops are most severe + overall_score = ( + temporal_score * weights[0] + + causal_score * weights[1] + + evidence_score * weights[2] + + quantum_score * weights[3] + ) + + return min(overall_score, 1.0) + + def _determine_paradox_status(self, score: float) -> ParadoxStatus: + """Determine paradox status based on score""" + if score >= 0.8: + return ParadoxStatus.FULL_PARADOX + elif score >= 0.6: + return ParadoxStatus.NEAR_PARADOX + else: + return ParadoxStatus.STABLE + + def _generate_resolution_recommendations(self, temporal: Dict, causal: Dict, evidence: Dict, quantum: Dict) -> List[Dict]: + """Generate paradox resolution recommendations""" + recommendations = [] + + # Temporal paradox resolutions + if temporal.get("paradox_detected", False): + recommendations.append({ + "paradox_type": "temporal", + "strategy": "temporal_damping", + "priority": "high" if temporal.get("score", 0) > 0.7 else "medium", + "description": "Apply temporal coherence damping to resolve time-based inconsistencies" + }) + + # Causal loop resolutions + if causal.get("paradox_detected", False): + recommendations.append({ + "paradox_type": "causal", + "strategy": "multiverse_resolution", + "priority": "critical", + "description": "Resolve causal loops through multiple timeline theory" + }) + + # Evidence contradiction resolutions + if evidence.get("paradox_detected", False): + recommendations.append({ + "paradox_type": "evidence", + "strategy": "evidence_reweighting", + "priority": "medium", + "description": "Re-evaluate evidence weights based on reliability and source quality" + }) + + # Quantum paradox resolutions + if quantum.get("paradox_detected", False): + recommendations.append({ + "paradox_type": "quantum", + "strategy": "quantum_decoherence", + "priority": "medium", + "description": "Force quantum state collapse to resolve superposition conflicts" + }) + + return recommendations + +# === COMPONENT 3: EPISTEMIC GROUNDING ENGINE === +class EpistemicGroundingEngine: + """Advanced epistemic grounding and justification system""" + + def __init__(self, performance_monitor=None): + self.justification_frameworks = self._initialize_justification_frameworks() + self.truth_criteria = self._initialize_truth_criteria() + self.knowledge_graph = KnowledgeGraph() + self.performance_monitor = performance_monitor + + if self.performance_monitor: + self.ground_claim = self.performance_monitor.track_performance(self.ground_claim) + + def _initialize_justification_frameworks(self) -> Dict: + """Initialize epistemic justification frameworks""" + return { + "foundationalism": { + "description": "Knowledge based on basic beliefs", + "validation_method": "basic_belief_verification", + "applicability": ["mathematics", "logic"] + }, + "coherentism": { + "description": "Knowledge as coherent belief systems", + "validation_method": "system_coherence_check", + "applicability": ["science", "philosophy"] + }, + "reliabilism": { + "description": "Knowledge from reliable processes", + "validation_method": "process_reliability_assessment", + "applicability": ["empirical_sciences"] + }, + "pragmatism": { + "description": "Knowledge based on practical consequences", + "validation_method": "practical_utility_assessment", + "applicability": ["technology", "applied_sciences"] + } + } + + def _initialize_truth_criteria(self) -> Dict: + """Initialize truth criteria across domains""" + return { + "correspondence": { + "description": "Truth as correspondence to reality", + "domains": ["science", "history"], + "validation_weight": 0.8 + }, + "coherence": { + "description": "Truth as coherence within system", + "domains": ["mathematics", "logic"], + "validation_weight": 0.9 + }, + "pragmatic": { + "description": "Truth as practical utility", + "domains": ["technology", "medicine"], + "validation_weight": 0.7 + }, + "consensus": { + "description": "Truth as expert consensus", + "domains": ["social_science", "philosophy"], + "validation_weight": 0.6 + } + } + + async def ground_claim(self, claim: UniversalClaim, context: Dict = None) -> Dict: + """Provide epistemic grounding for claims""" + try: + grounding_tasks = await asyncio.gather( + self._assess_justification(claim), + self._evaluate_truth_criteria(claim), + self._verify_epistemic_foundations(claim), + self._analyze_knowledge_integration(claim), + return_exceptions=True + ) + + # Process grounding results + justification = self._handle_grounding_result(grounding_tasks[0]) + truth_evaluation = self._handle_grounding_result(grounding_tasks[1]) + foundations = self._handle_grounding_result(grounding_tasks[2]) + integration = self._handle_grounding_result(grounding_tasks[3]) + + # Calculate epistemic grounding score + grounding_score = self._calculate_grounding_score(justification, truth_evaluation, foundations, integration) + + return { + "justification_analysis": justification, + "truth_evaluation": truth_evaluation, + "epistemic_foundations": foundations, + "knowledge_integration": integration, + "epistemic_grounding_score": grounding_score, + "grounding_status": self._determine_grounding_status(grounding_score), + "warrant_level": self._assess_warrant_level(grounding_score), + "recommended_actions": self._generate_epistemic_actions(grounding_score, claim) + } + + except Exception as e: + logger.error(f"Epistemic grounding failed: {e}") + return { + "justification_analysis": {"error": str(e)}, + "truth_evaluation": {"error": str(e)}, + "epistemic_foundations": {"error": str(e)}, + "knowledge_integration": {"error": str(e)}, + "epistemic_grounding_score": 0.3, + "grounding_status": "ungrounded", + "warrant_level": "insufficient", + "recommended_actions": ["investigate_epistemic_failure"] + } + + def _handle_grounding_result(self, result: Any) -> Dict: + """Handle grounding results with error checking""" + if isinstance(result, Exception): + return {"error": str(result), "score": 0.3} + return result + + async def _assess_justification(self, claim: UniversalClaim) -> Dict: + """Assess epistemic justification for claim""" + try: + justification_scores = {} + + # Evaluate different justification frameworks + for framework_name, framework in self.justification_frameworks.items(): + score = self._evaluate_framework_justification(claim, framework) + justification_scores[framework_name] = score + + # Determine optimal justification framework + optimal_framework = max(justification_scores.items(), key=lambda x: x[1]) + + return { + "framework_scores": justification_scores, + "optimal_framework": optimal_framework[0], + "optimal_score": optimal_framework[1], + "justification_strength": optimal_framework[1], + "analysis_method": "multi_framework_justification_assessment" + } + except Exception as e: + logger.warning(f"Justification assessment failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _evaluate_truth_criteria(self, claim: UniversalClaim) -> Dict: + """Evaluate claim against truth criteria""" + try: + truth_scores = {} + + for criterion_name, criterion in self.truth_criteria.items(): + score = self._evaluate_truth_criterion(claim, criterion) + truth_scores[criterion_name] = score + + # Calculate weighted truth score + weighted_score = self._calculate_weighted_truth_score(truth_scores, claim) + + return { + "criterion_scores": truth_scores, + "weighted_truth_score": weighted_score, + "primary_truth_criterion": max(truth_scores.items(), key=lambda x: x[1])[0], + "truth_coherence": np.std(list(truth_scores.values())) if truth_scores else 0.0 + } + except Exception as e: + logger.warning(f"Truth evaluation failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _verify_epistemic_foundations(self, claim: UniversalClaim) -> Dict: + """Verify epistemic foundations of claim""" + try: + foundation_checks = {} + + # Check evidence foundations + evidence_foundation = self._check_evidence_foundations(claim) + foundation_checks["evidence_foundation"] = evidence_foundation + + # Check reasoning foundations + reasoning_foundation = self._check_reasoning_foundations(claim) + foundation_checks["reasoning_foundation"] = reasoning_foundation + + # Check domain foundations + domain_foundation = self._check_domain_foundations(claim) + foundation_checks["domain_foundation"] = domain_foundation + + overall_score = np.mean([check.get("score", 0.0) for check in foundation_checks.values()]) + + return { + "foundation_checks": foundation_checks, + "overall_foundation_score": overall_score, + "foundation_strength": "strong" if overall_score > 0.8 else "adequate" if overall_score > 0.6 else "weak", + "critical_issues": self._identify_critical_foundation_issues(foundation_checks) + } + except Exception as e: + logger.warning(f"Foundation verification failed: {e}") + return {"error": str(e), "score": 0.3} + + async def _analyze_knowledge_integration(self, claim: UniversalClaim) -> Dict: + """Analyze integration with existing knowledge""" + try: + integration_metrics = {} + + # Check coherence with knowledge graph + graph_coherence = await self.knowledge_graph.check_coherence(claim) + integration_metrics["knowledge_graph_coherence"] = graph_coherence + + # Check domain integration + domain_integration = self._check_domain_integration(claim) + integration_metrics["domain_integration"] = domain_integration + + # Check explanatory power + explanatory_power = self._assess_explanatory_power(claim) + integration_metrics["explanatory_power"] = explanatory_power + + overall_integration = np.mean([metric.get("score", 0.0) for metric in integration_metrics.values()]) + + return { + "integration_metrics": integration_metrics, + "overall_integration_score": overall_integration, + "integration_quality": "seamless" if overall_integration > 0.8 else "good" if overall_integration > 0.6 else "problematic", + "integration_issues": self._identify_integration_issues(integration_metrics) + } + except Exception as e: + logger.warning(f"Knowledge integration analysis failed: {e}") + return {"error": str(e), "score": 0.3} + + def _evaluate_framework_justification(self, claim: UniversalClaim, framework: Dict) -> float: + """Evaluate claim against specific justification framework""" + framework_score = 0.0 + + # Foundationalism evaluation + if framework["validation_method"] == "basic_belief_verification": + basic_beliefs = self._identify_basic_beliefs(claim) + framework_score = len(basic_beliefs) / max(len(claim.evidence_chain), 1) + + # Coherentism evaluation + elif framework["validation_method"] == "system_coherence_check": + coherence = self._calculate_system_coherence(claim) + framework_score = coherence + + # Reliabilism evaluation + elif framework["validation_method"] == "process_reliability_assessment": + reliability = np.mean([e.reliability for e in claim.evidence_chain]) if claim.evidence_chain else 0.0 + framework_score = reliability + + # Pragmatism evaluation + elif framework["validation_method"] == "practical_utility_assessment": + utility = self._assess_practical_utility(claim) + framework_score = utility + + return min(framework_score, 1.0) + + def _evaluate_truth_criterion(self, claim: UniversalClaim, criterion: Dict) -> float: + """Evaluate claim against specific truth criterion""" + criterion_score = 0.0 + + if criterion["description"] == "Truth as correspondence to reality": + # Assess empirical correspondence + empirical_evidence = [e for e in claim.evidence_chain if e.domain in [KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY]] + if empirical_evidence: + criterion_score = np.mean([e.weighted_strength() for e in empirical_evidence]) + + elif criterion["description"] == "Truth as coherence within system": + # Assess logical coherence + coherence = self._calculate_logical_coherence(claim) + criterion_score = coherence + + elif criterion["description"] == "Truth as practical utility": + # Assess practical utility + utility = self._assess_practical_utility(claim) + criterion_score = utility + + elif criterion["description"] == "Truth as expert consensus": + # Assess consensus alignment + consensus = self._assess_consensus_alignment(claim) + criterion_score = consensus + + return min(criterion_score, 1.0) + + def _calculate_weighted_truth_score(self, truth_scores: Dict, claim: UniversalClaim) -> float: + """Calculate weighted truth score based on claim domains""" + domain_weights = {} + + # Assign weights based on claim domains + for domain in claim.sub_domains: + if domain == KnowledgeDomain.SCIENCE: + domain_weights["correspondence"] = 0.6 + domain_weights["coherence"] = 0.3 + domain_weights["pragmatic"] = 0.1 + elif domain == KnowledgeDomain.MATHEMATICS: + domain_weights["coherence"] = 0.9 + domain_weights["correspondence"] = 0.1 + elif domain == KnowledgeDomain.TECHNOLOGY: + domain_weights["pragmatic"] = 0.7 + domain_weights["correspondence"] = 0.2 + domain_weights["coherence"] = 0.1 + + # Default weights if no specific domain mapping + if not domain_weights: + domain_weights = {"correspondence": 0.4, "coherence": 0.3, "pragmatic": 0.2, "consensus": 0.1} + + # Calculate weighted score + weighted_score = 0.0 + total_weight = 0.0 + + for criterion, score in truth_scores.items(): + weight = domain_weights.get(criterion, 0.1) + weighted_score += score * weight + total_weight += weight + + return weighted_score / total_weight if total_weight > 0 else 0.5 + + def _check_evidence_foundations(self, claim: UniversalClaim) -> Dict: + """Check foundations of evidence chain""" + if not claim.evidence_chain: + return {"score": 0.1, "issues": ["No evidence provided"], "status": "critical"} + + evidence_scores = [] + issues = [] + + for evidence in claim.evidence_chain: + evidence_score = evidence.evidence_quality_score() + evidence_scores.append(evidence_score) + + if evidence_score < 0.3: + issues.append(f"Weak evidence: {evidence.evidence_id}") + if evidence.contradictory: + issues.append(f"Contradictory evidence: {evidence.evidence_id}") + + avg_score = np.mean(evidence_scores) if evidence_scores else 0.0 + + return { + "score": avg_score, + "issues": issues, + "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak", + "evidence_count": len(claim.evidence_chain) + } + + def _check_reasoning_foundations(self, claim: UniversalClaim) -> Dict: + """Check foundations of reasoning modes""" + if not claim.reasoning_modes: + return {"score": 0.1, "issues": ["No reasoning modes specified"], "status": "critical"} + + reasoning_scores = [] + issues = [] + + for reasoning_mode in claim.reasoning_modes: + mode_score = self._evaluate_reasoning_mode(reasoning_mode, claim) + reasoning_scores.append(mode_score) + + if mode_score < 0.4: + issues.append(f"Problematic reasoning mode: {reasoning_mode.value}") + + avg_score = np.mean(reasoning_scores) if reasoning_scores else 0.0 + + return { + "score": avg_score, + "issues": issues, + "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak", + "reasoning_modes_used": len(claim.reasoning_modes) + } + + def _check_domain_foundations(self, claim: UniversalClaim) -> Dict: + """Check domain-specific foundations""" + if not claim.sub_domains: + return {"score": 0.1, "issues": ["No domains specified"], "status": "critical"} + + domain_scores = [] + issues = [] + + for domain in claim.sub_domains: + domain_score = self._evaluate_domain_foundation(domain, claim) + domain_scores.append(domain_score) + + if domain_score < 0.5: + issues.append(f"Weak foundation in domain: {domain.value}") + + avg_score = np.mean(domain_scores) if domain_scores else 0.0 + + return { + "score": avg_score, + "issues": issues, + "status": "strong" if avg_score > 0.8 else "adequate" if avg_score > 0.6 else "weak", + "domains_covered": len(claim.sub_domains) + } + + def _evaluate_reasoning_mode(self, reasoning_mode: ReasoningMode, claim: UniversalClaim) -> float: + """Evaluate appropriateness of reasoning mode for claim""" + mode_scores = { + ReasoningMode.DEDUCTIVE: 0.8, # Generally strong + ReasoningMode.INDUCTIVE: 0.7, # Good for empirical claims + ReasoningMode.ABDUCTIVE: 0.6, # Explanatory power + ReasoningMode.BAYESIAN: 0.8, # Probabilistic reasoning + ReasoningMode.CAUSAL: 0.7, # Causal analysis + ReasoningMode.QUANTUM: 0.5, # Specialized + ReasoningMode.RETROCAUSAL: 0.4 # Experimental + } + + return mode_scores.get(reasoning_mode, 0.5) + + def _evaluate_domain_foundation(self, domain: KnowledgeDomain, claim: UniversalClaim) -> float: + """Evaluate domain foundation strength""" + # Check if evidence supports domain claims + domain_evidence = [e for e in claim.evidence_chain if e.domain == domain] + + if not domain_evidence: + return 0.3 # No domain-specific evidence + + # Calculate average evidence strength for domain + avg_strength = np.mean([e.weighted_strength() for e in domain_evidence]) + return min(avg_strength, 1.0) + + def _identify_basic_beliefs(self, claim: UniversalClaim) -> List[Evidence]: + """Identify basic beliefs in evidence chain""" + basic_beliefs = [] + + for evidence in claim.evidence_chain: + # Basic beliefs are high-reliability, direct evidence + if evidence.reliability > 0.8 and evidence.source_quality > 0.8: + basic_beliefs.append(evidence) + + return basic_beliefs + + def _calculate_system_coherence(self, claim: UniversalClaim) -> float: + """Calculate system coherence of claim""" + if len(claim.evidence_chain) < 2: + return 0.5 + + # Calculate coherence between evidence items + coherence_scores = [] + for i, evidence1 in enumerate(claim.evidence_chain): + for j, evidence2 in enumerate(claim.evidence_chain[i+1:], i+1): + if not self._are_contradictory(evidence1, evidence2): + coherence = 1.0 - abs(evidence1.weighted_strength() - evidence2.weighted_strength()) + coherence_scores.append(coherence) + + return np.mean(coherence_scores) if coherence_scores else 0.5 + + def _calculate_logical_coherence(self, claim: UniversalClaim) -> float: + """Calculate logical coherence of claim""" + # Simplified logical coherence assessment + contradictory_count = sum(1 for e in claim.evidence_chain if e.contradictory) + total_evidence = len(claim.evidence_chain) + + if total_evidence == 0: + return 0.5 + + coherence = 1.0 - (contradictory_count / total_evidence) + return coherence + + def _assess_practical_utility(self, claim: UniversalClaim) -> float: + """Assess practical utility of claim""" + # Check if claim has practical applications + utility_indicators = ["application", "utility", "practical", "implementation", "use"] + claim_lower = claim.content.lower() + + indicator_count = sum(1 for indicator in utility_indicators if indicator in claim_lower) + utility_score = min(indicator_count / len(utility_indicators), 1.0) + + return utility_score + + def _assess_consensus_alignment(self, claim: UniversalClaim) -> float: + """Assess alignment with expert consensus""" + # Simplified consensus assessment + high_quality_evidence = [e for e in claim.evidence_chain if e.source_quality > 0.7 and e.reliability > 0.7] + + if not claim.evidence_chain: + return 0.3 + + consensus_alignment = len(high_quality_evidence) / len(claim.evidence_chain) + return consensus_alignment + + def _identify_critical_foundation_issues(self, foundation_checks: Dict) -> List[str]: + """Identify critical foundation issues""" + critical_issues = [] + + for check_type, check_result in foundation_checks.items(): + if check_result.get("score", 0) < 0.4: + critical_issues.append(f"Critical {check_type} issues") + critical_issues.extend([f"{check_type}: {issue}" for issue in check_result.get("issues", []) if "critical" in issue.lower()]) + + return critical_issues + + def _check_domain_integration(self, claim: UniversalClaim) -> Dict: + """Check integration across domains""" + if len(claim.sub_domains) < 2: + return {"score": 0.5, "description": "Single-domain claim", "integration_level": "minimal"} + + # Assess cross-domain coherence + domain_evidence = {} + for domain in claim.sub_domains: + domain_evidence[domain] = [e for e in claim.evidence_chain if e.domain == domain] + + # Calculate integration score based on evidence distribution + evidence_counts = [len(evidence) for evidence in domain_evidence.values()] + if not evidence_counts: + return {"score": 0.3, "description": "No domain evidence", "integration_level": "poor"} + + integration_score = min(np.std(evidence_counts) / np.mean(evidence_counts), 1.0) if np.mean(evidence_counts) > 0 else 0.5 + + return { + "score": 1.0 - integration_score, # Lower variance = better integration + "description": f"Integration across {len(claim.sub_domains)} domains", + "integration_level": "strong" if integration_score < 0.3 else "moderate" if integration_score < 0.6 else "weak" + } + + def _assess_explanatory_power(self, claim: UniversalClaim) -> Dict: + """Assess explanatory power of claim""" + # Check for explanatory elements + explanatory_indicators = ["explains", "causes", "leads to", "results in", "because", "therefore"] + claim_lower = claim.content.lower() + + indicator_count = sum(1 for indicator in explanatory_indicators if indicator in claim_lower) + explanatory_density = indicator_count / len(explanatory_indicators) + + # Consider causal mechanisms + causal_strength = len(claim.causal_mechanisms) / max(len(claim.causal_mechanisms) + 1, 5) + + explanatory_score = (explanatory_density + causal_strength) / 2 + + return { + "score": explanatory_score, + "description": f"Explanatory power with {len(claim.causal_mechanisms)} causal mechanisms", + "explanatory_level": "strong" if explanatory_score > 0.7 else "moderate" if explanatory_score > 0.5 else "weak" + } + + def _identify_integration_issues(self, integration_metrics: Dict) -> List[str]: + """Identify knowledge integration issues""" + issues = [] + + for metric_name, metric_result in integration_metrics.items(): + if metric_result.get("score", 0) < 0.5: + issues.append(f"Poor {metric_name.replace('_', ' ')}") + + return issues + + def _calculate_grounding_score(self, justification: Dict, truth_evaluation: Dict, foundations: Dict, integration: Dict) -> float: + """Calculate overall epistemic grounding score""" + justification_score = justification.get("justification_strength", 0.5) + truth_score = truth_evaluation.get("weighted_truth_score", 0.5) + foundation_score = foundations.get("overall_foundation_score", 0.5) + integration_score = integration.get("overall_integration_score", 0.5) + + weights = [0.3, 0.3, 0.2, 0.2] + grounding_score = ( + justification_score * weights[0] + + truth_score * weights[1] + + foundation_score * weights[2] + + integration_score * weights[3] + ) + + return min(grounding_score, 1.0) + + def _determine_grounding_status(self, score: float) -> str: + """Determine epistemic grounding status""" + if score >= 0.9: + return "FULLY_GROUNDED" + elif score >= 0.8: + return "WELL_GROUNDED" + elif score >= 0.7: + return "ADEQUATELY_GROUNDED" + elif score >= 0.6: + return "PARTIALLY_GROUNDED" + elif score >= 0.5: + return "WEAKLY_GROUNDED" + else: + return "UNGROUNDED" + + def _assess_warrant_level(self, grounding_score: float) -> str: + """Assess warrant level for belief""" + if grounding_score >= 0.9: + return "COMPLETE_WARRANT" + elif grounding_score >= 0.8: + return "STRONG_WARRANT" + elif grounding_score >= 0.7: + return "ADEQUATE_WARRANT" + elif grounding_score >= 0.6: + return "PARTIAL_WARRANT" + elif grounding_score >= 0.5: + return "MINIMAL_WARRANT" + else: + return "INSUFFICIENT_WARRANT" + + def _generate_epistemic_actions(self, grounding_score: float, claim: UniversalClaim) -> List[str]: + """Generate epistemic improvement actions""" + actions = [] + + if grounding_score < 0.7: + actions.append("Strengthen evidence foundation with higher-quality sources") + + if grounding_score < 0.6: + actions.append("Improve justification through multiple epistemic frameworks") + + if len(claim.evidence_chain) < 3: + actions.append("Gather additional supporting evidence") + + if any(e.contradictory for e in claim.evidence_chain): + actions.append("Resolve evidence contradictions") + + if not actions: + actions.append("Maintain current epistemic standards") + + return actions + +# === COMPONENT 4: KNOWLEDGE GRAPH INTEGRATION === +class KnowledgeGraph: + """Knowledge graph for coherence checking and integration""" + + def __init__(self): + self.nodes = {} + self.edges = defaultdict(list) + self.domain_knowledge = self._initialize_domain_knowledge() + + def _initialize_domain_knowledge(self) -> Dict: + """Initialize domain-specific knowledge bases""" + return { + KnowledgeDomain.SCIENCE: { + "principles": ["empirical_verification", "falsifiability", "reproducibility"], + "methods": ["experimentation", "observation", "measurement"], + "standards": ["peer_review", "statistical_significance"] + }, + KnowledgeDomain.MATHEMATICS: { + "principles": ["logical_consistency", "proof", "axiomatic_systems"], + "methods": ["deduction", "proof", "abstraction"], + "standards": ["rigor", "precision", "completeness"] + }, + KnowledgeDomain.PHILOSOPHY: { + "principles": ["logical_coherence", "conceptual_clarity", "argument_strength"], + "methods": ["analysis", "synthesis", "critique"], + "standards": ["rational_justification", "systematic_inquiry"] + } + } + + async def check_coherence(self, claim: UniversalClaim) -> Dict: + """Check coherence with existing knowledge graph""" + try: + coherence_checks = {} + + # Check domain coherence + domain_coherence = self._check_domain_coherence(claim) + coherence_checks["domain_coherence"] = domain_coherence + + # Check logical coherence + logical_coherence = self._check_logical_coherence(claim) + coherence_checks["logical_coherence"] = logical_coherence + + # Check evidence coherence + evidence_coherence = self._check_evidence_coherence(claim) + coherence_checks["evidence_coherence"] = evidence_coherence + + overall_coherence = np.mean([check.get("score", 0.0) for check in coherence_checks.values()]) + + return { + "coherence_checks": coherence_checks, + "overall_coherence_score": overall_coherence, + "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low", + "integration_issues": self._identify_coherence_issues(coherence_checks) + } + except Exception as e: + logger.warning(f"Knowledge graph coherence check failed: {e}") + return {"error": str(e), "score": 0.3} + + def _check_domain_coherence(self, claim: UniversalClaim) -> Dict: + """Check coherence with domain knowledge""" + domain_scores = [] + + for domain in claim.sub_domains: + if domain in self.domain_knowledge: + domain_score = self._evaluate_domain_alignment(claim, domain) + domain_scores.append(domain_score) + + avg_score = np.mean(domain_scores) if domain_scores else 0.5 + + return { + "score": avg_score, + "domains_evaluated": len(domain_scores), + "alignment": "strong" if avg_score > 0.8 else "moderate" if avg_score > 0.6 else "weak" + } + + def _check_logical_coherence(self, claim: UniversalClaim) -> Dict: + """Check logical coherence within claim structure""" + # Evaluate reasoning mode coherence + reasoning_coherence = self._evaluate_reasoning_coherence(claim) + + # Evaluate causal mechanism coherence + causal_coherence = self._evaluate_causal_coherence(claim) + + overall_coherence = (reasoning_coherence + causal_coherence) / 2 + + return { + "score": overall_coherence, + "reasoning_coherence": reasoning_coherence, + "causal_coherence": causal_coherence, + "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low" + } + + def _check_evidence_coherence(self, claim: UniversalClaim) -> Dict: + """Check coherence of evidence chain""" + if not claim.evidence_chain: + return {"score": 0.3, "description": "No evidence to evaluate", "coherence_level": "poor"} + + # Calculate evidence consistency + consistent_evidence = [e for e in claim.evidence_chain if not e.contradictory] + consistency_ratio = len(consistent_evidence) / len(claim.evidence_chain) + + # Calculate evidence strength coherence + strengths = [e.weighted_strength() for e in claim.evidence_chain] + strength_coherence = 1.0 - (np.std(strengths) / np.mean(strengths)) if np.mean(strengths) > 0 else 0.5 + + overall_coherence = (consistency_ratio + strength_coherence) / 2 + + return { + "score": overall_coherence, + "consistency_ratio": consistency_ratio, + "strength_coherence": strength_coherence, + "coherence_level": "high" if overall_coherence > 0.8 else "moderate" if overall_coherence > 0.6 else "low" + } + + def _evaluate_domain_alignment(self, claim: UniversalClaim, domain: KnowledgeDomain) -> float: + """Evaluate alignment with domain-specific standards""" + domain_knowledge = self.domain_knowledge.get(domain, {}) + + alignment_scores = [] + + # Check principle alignment + for principle in domain_knowledge.get("principles", []): + principle_score = self._check_principle_alignment(claim, principle) + alignment_scores.append(principle_score) + + # Check method alignment + for method in domain_knowledge.get("methods", []): + method_score = self._check_method_alignment(claim, method) + alignment_scores.append(method_score) + + return np.mean(alignment_scores) if alignment_scores else 0.5 + + def _check_principle_alignment(self, claim: UniversalClaim, principle: str) -> float: + """Check alignment with specific principle""" + principle_mapping = { + "empirical_verification": 0.8 if any(e.domain in [KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY] for e in claim.evidence_chain) else 0.3, + "falsifiability": 0.7 if any("test" in cm.lower() or "falsif" in cm.lower() for cm in claim.causal_mechanisms) else 0.4, + "logical_consistency": 0.9 if not any(e.contradictory for e in claim.evidence_chain) else 0.5, + "conceptual_clarity": 0.7 if len(claim.content.split()) < 100 else 0.5 # Simplicity heuristic + } + + return principle_mapping.get(principle, 0.5) + + def _check_method_alignment(self, claim: UniversalClaim, method: str) -> float: + """Check alignment with specific method""" + method_mapping = { + "experimentation": 0.8 if any("experiment" in e.content.lower() for e in claim.evidence_chain) else 0.4, + "deduction": 0.9 if ReasoningMode.DEDUCTIVE in claim.reasoning_modes else 0.5, + "observation": 0.7 if any("observe" in e.content.lower() for e in claim.evidence_chain) else 0.4, + "analysis": 0.8 if any("analyze" in e.content.lower() or "analysis" in e.content.lower() for e in claim.evidence_chain) else 0.5 + } + + return method_mapping.get(method, 0.5) + + def _evaluate_reasoning_coherence(self, claim: UniversalClaim) -> float: + """Evaluate coherence of reasoning modes""" + if not claim.reasoning_modes: + return 0.3 + + # Check for complementary reasoning modes + complementary_pairs = [ + (ReasoningMode.DEDUCTIVE, ReasoningMode.INDUCTIVE), + (ReasoningMode.ABDUCTIVE, ReasoningMode.CAUSAL), + (ReasoningMode.BAYESIAN, ReasoningMode.QUANTUM) + ] + + complementary_score = 0.0 + for mode1, mode2 in complementary_pairs: + if mode1 in claim.reasoning_modes and mode2 in claim.reasoning_modes: + complementary_score += 0.2 + + # Normalize score + reasoning_coherence = min(complementary_score, 1.0) + return reasoning_coherence + + def _evaluate_causal_coherence(self, claim: UniversalClaim) -> float: + """Evaluate coherence of causal mechanisms""" + if not claim.causal_mechanisms: + return 0.3 + + # Check for logical consistency in causal mechanisms + mechanism_keywords = ["cause", "effect", "lead to", "result in", "because", "therefore"] + mechanism_count = sum(1 for mechanism in claim.causal_mechanisms + if any(keyword in mechanism.lower() for keyword in mechanism_keywords))