import aiohttp import asyncio import numpy as np import math import logging import time import psutil from datetime import datetime, timedelta from typing import Dict, List, Tuple, Optional, Union from dataclasses import dataclass, field from enum import Enum import json import hashlib from contextlib import asynccontextmanager # Configure logging with better formatting logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s', handlers=[ logging.StreamHandler(), logging.FileHandler("agi_validator.log", mode='a') ] ) logger = logging.getLogger("AGI_Validator") # -------------------------- # ENUMERATION COMPONENTS # -------------------------- class ValidationStatus(Enum): """Enumeration for validation statuses""" SUCCESS = "success" PARTIAL_SUCCESS = "partial_success" FAILURE = "failure" ERROR = "error" class ReasoningMode(Enum): """Enumeration for reasoning modes""" DEDUCTIVE = "deductive" INDUCTIVE = "inductive" ABDUCTIVE = "abductive" BAYESIAN = "bayesian" CAUSAL = "causal" class KnowledgeDomain(Enum): """Enumeration for knowledge domains""" SCIENCE = "science" MATHEMATICS = "mathematics" PHILOSOPHY = "philosophy" HISTORY = "history" MEDICINE = "medicine" TECHNOLOGY = "technology" SOCIAL_SCIENCE = "social_science" # -------------------------- # DATA MODEL COMPONENTS # -------------------------- @dataclass class Evidence: """Enhanced evidence representation with validation""" evidence_id: str strength: float reliability: float source_quality: float = 0.8 contradictory: bool = False timestamp: datetime = field(default_factory=datetime.utcnow) domain: Optional[KnowledgeDomain] = None def __post_init__(self): """Validate evidence parameters""" if not (0.0 <= self.strength <= 1.0): raise ValueError("Evidence strength must be between 0.0 and 1.0") if not (0.0 <= self.reliability <= 1.0): raise ValueError("Evidence reliability must be between 0.0 and 1.0") if not (0.0 <= self.source_quality <= 1.0): raise ValueError("Source quality must be between 0.0 and 1.0") @property def weighted_strength(self) -> float: """Calculate weighted strength based on reliability and source quality""" return self.strength * self.reliability * self.source_quality def to_dict(self) -> Dict: """Convert to dictionary for serialization""" return { 'evidence_id': self.evidence_id, 'strength': self.strength, 'reliability': self.reliability, 'source_quality': self.source_quality, 'contradictory': self.contradictory, 'timestamp': self.timestamp.isoformat(), 'domain': self.domain.value if self.domain else None, 'weighted_strength': self.weighted_strength } @dataclass class UniversalClaim: """Enhanced claim representation with better validation""" claim_id: str content: str evidence_chain: List[Evidence] = field(default_factory=list) reasoning_modes: List[ReasoningMode] = field(default_factory=list) sub_domains: List[KnowledgeDomain] = field(default_factory=list) causal_mechanisms: List[str] = field(default_factory=list) expected_validity: Optional[float] = None metadata: Dict = field(default_factory=dict) def __post_init__(self): """Validate claim parameters""" if not self.content.strip(): raise ValueError("Claim content cannot be empty") if self.expected_validity is not None: if not (0.0 <= self.expected_validity <= 1.0): raise ValueError("Expected validity must be between 0.0 and 1.0") # Generate hash-based ID if not provided if not self.claim_id: self.claim_id = self._generate_claim_id() def _generate_claim_id(self) -> str: """Generate unique claim ID based on content hash""" content_hash = hashlib.md5(self.content.encode()).hexdigest() return f"claim_{content_hash[:12]}" @property def evidence_summary(self) -> Dict: """Get summary statistics of evidence""" if not self.evidence_chain: return {'count': 0, 'avg_strength': 0.0, 'avg_reliability': 0.0} strengths = [e.weighted_strength for e in self.evidence_chain] reliabilities = [e.reliability for e in self.eidence_chain] return { 'count': len(self.evidence_chain), 'avg_strength': np.mean(strengths), 'avg_reliability': np.mean(reliabilities), 'contradictory_count': sum(1 for e in self.evidence_chain if e.contradictory) } def to_dict(self) -> Dict: """Convert to dictionary for serialization""" return { 'claim_id': self.claim_id, 'content': self.content, 'evidence_chain': [e.to_dict() for e in self.evidence_chain], 'reasoning_modes': [m.value for m in self.reasoning_modes], 'sub_domains': [d.value for d in self.sub_domains], 'causal_mechanisms': self.causal_mechanisms, 'expected_validity': self.expected_validity, 'evidence_summary': self.evidence_summary, 'metadata': self.metadata } # -------------------------- # CORE VALIDATION COMPONENT # -------------------------- class AdvancedGeneralIntelligence: """Enhanced AGI validation system with improved architecture""" def __init__(self, mcp_enabled: bool = True, mcp_timeout: int = 15, max_history: int = 100, cache_enabled: bool = True): self.mcp_enabled = mcp_enabled self.mcp_timeout = mcp_timeout self.max_history = max_history self.cache_enabled = cache_enabled self.mcp_url = "https://agents-mcp-hackathon-consilium-mcp.hf.space/run/predict" self.validation_history = [] self.validation_cache = {} self.test_cases = self._initialize_test_cases() self._session = None logger.info("Enhanced AGI Validator initialized") # -------------------------- # NETWORK COMPONENT # -------------------------- @asynccontextmanager async def _get_session(self): """Context manager for HTTP session""" if self._session is None: connector = aiohttp.TCPConnector(limit=10, limit_per_host=5) timeout = aiohttp.ClientTimeout(total=self.mcp_timeout) self._session = aiohttp.ClientSession(connector=connector, timeout=timeout) try: yield self._session except Exception as e: logger.error(f"Session error: {e}") raise async def close(self): """Clean up resources""" if self._session: await self._session.close() self._session = None # -------------------------- # CACHING COMPONENT # -------------------------- def _get_cache_key(self, claim: UniversalClaim) -> str: """Generate cache key for claim""" claim_data = claim.to_dict() claim_json = json.dumps(claim_data, sort_keys=True) return hashlib.sha256(claim_json.encode()).hexdigest() # -------------------------- # MCP CONSENSUS COMPONENT # -------------------------- async def _get_mcp_consensus(self, claim: UniversalClaim) -> Dict: """Enhanced mCP consensus with caching and better error handling""" if not self.mcp_enabled: logger.info("mCP consensus protocol disabled") return self._get_fallback_consensus("mCP disabled") # Check cache first cache_key = self._get_cache_key(claim) if self.cache_enabled else None if cache_key and cache_key in self.validation_cache: logger.info("Using cached mCP consensus") return self.validation_cache[cache_key] payload = { "claim_text": claim.content, "domains": [d.value for d in claim.sub_domains], "reasoning_modes": [m.value for m in claim.reasoning_modes], "evidence_count": len(claim.evidence_chain), "evidence_summary": claim.evidence_summary, "causal_mechanisms": claim.causal_mechanisms, "validation_mode": "full_mesh", "rounds": 3 } start_time = time.monotonic() try: async with self._get_session() as session: async with session.post(self.mcp_url, json=payload) as response: if response.status == 200: result = await response.json() elapsed = time.monotonic() - start_time mcp_result = { **result.get("data", {}), "processing_time": elapsed, "reliability": 1.0, "cache_hit": False } # Cache the result if cache_key: self.validation_cache[cache_key] = mcp_result logger.info(f"mCP consensus received in {elapsed:.2f}s") return mcp_result else: logger.warning(f"mCP returned status {response.status}") return self._get_fallback_consensus(f"HTTP {response.status}") except asyncio.TimeoutError: logger.warning("mCP request timed out") return self._get_fallback_consensus("timeout") except aiohttp.ClientError as e: logger.error(f"HTTP error in mCP request: {str(e)}") return self._get_fallback_consensus(f"client_error: {str(e)}") except Exception as e: logger.exception(f"Unexpected error in mCP request: {str(e)}") return self._get_fallback_consensus(f"unexpected_error: {str(e)}") def _get_fallback_consensus(self, reason: str = "unknown") -> Dict: """Enhanced fallback consensus with reason tracking""" return { "consensus_score": 0.5, "confidence_interval": [0.4, 0.6], "expert_notes": [f"Consensus service unavailable: {reason}"], "reliability": 0.0, "processing_time": 0.0, "fallback_reason": reason } # -------------------------- # REASONING ANALYTICS COMPONENT # -------------------------- async def _perform_reasoning_analysis(self, claim: UniversalClaim) -> Dict: """Enhanced reasoning analysis with multiple reasoning modes""" start_time = time.monotonic() try: results = {} # Bayesian reasoning if ReasoningMode.BAYESIAN in claim.reasoning_modes: prior = 0.5 # Neutral prior evidence_weights = [e.weighted_strength for e in claim.evidence_chain] if evidence_weights: likelihood = np.mean(evidence_weights) # Simplified Bayesian update posterior = (likelihood * prior) / ((likelihood * prior) + ((1 - likelihood) * (1 - prior))) results['bayesian'] = { 'prior': prior, 'likelihood': likelihood, 'posterior': posterior } # Causal reasoning if ReasoningMode.CAUSAL in claim.reasoning_modes: causal_strength = len(claim.causal_mechanisms) / max(5, len(claim.causal_mechanisms)) results['causal'] = { 'causal_coherence': min(0.95, 0.5 + causal_strength * 0.4), 'mechanism_count': len(claim.causal_mechanisms) } # Deductive reasoning if ReasoningMode.DEDUCTIVE in claim.reasoning_modes: # Simple logical consistency check contradictory_evidence = sum(1 for e in claim.evidence_chain if e.contradictory) consistency = max(0.1, 1.0 - (contradictory_evidence / max(1, len(claim.evidence_chain))) results['deductive'] = {'logical_consistency': consistency} processing_time = time.monotonic() - start_time return { **results, 'processing_time': processing_time, 'reasoning_modes_used': [m.value for m in claim.reasoning_modes] } except Exception as e: logger.error(f"Reasoning analysis failed: {str(e)}") return { 'error': f"Reasoning analysis failed: {str(e)}", 'processing_time': time.monotonic() - start_time } # -------------------------- # EVIDENCE ANALYTICS COMPONENT # -------------------------- async def _analyze_evidence_quality(self, claim: UniversalClaim) -> Dict: """Enhanced evidence quality analysis""" start_time = time.monotonic() try: if not claim.evidence_chain: return { 'evidence_score': 0.0, 'evidence_count': 0, 'quality_factors': {'no_evidence': True}, 'processing_time': time.monotonic() - start_time } # Calculate various evidence metrics strengths = [e.weighted_strength for e in claim.evidence_chain] reliabilities = [e.reliability for e in claim.evidence_chain] source_qualities = [e.source_quality for e in claim.evidence_chain] # Evidence diversity (different domains) domains = set(e.domain for e in claim.evidence_chain if e.domain) domain_diversity = len(domains) / max(1, len(KnowledgeDomain)) # Contradiction penalty contradictory_count = sum(1 for e in claim.evidence_chain if e.contradictory) contradiction_penalty = contradictory_count / len(claim.evidence_chain) # Overall evidence score base_score = np.mean(strengths) reliability_bonus = (np.mean(reliabilities) - 0.5) * 0.2 source_bonus = (np.mean(source_qualities) - 0.5) * 0.1 diversity_bonus = domain_diversity * 0.1 evidence_score = max(0.0, min(1.0, base_score + reliability_bonus + source_bonus + diversity_bonus - contradiction_penalty )) return { 'evidence_score': evidence_score, 'evidence_count': len(claim.evidence_chain), 'quality_factors': { 'base_score': base_score, 'reliability_bonus': reliability_bonus, 'source_bonus': source_bonus, 'diversity_bonus': diversity_bonus, 'contradiction_penalty': contradiction_penalty, 'domain_diversity': domain_diversity }, 'processing_time': time.monotonic() - start_time } except Exception as e: logger.error(f"Evidence analysis failed: {str(e)}") return { 'evidence_score': 0.5, 'evidence_count': len(claim.evidence_chain), 'error': str(e), 'processing_time': time.monotonic() - start_time } # -------------------------- # METACOGNITIVE ANALYTICS COMPONENT # -------------------------- async def _metacognitive_assessment(self, claim: UniversalClaim) -> Dict: """Enhanced metacognitive assessment""" start_time = time.monotonic() try: biases_detected = [] # Confirmation bias detection if claim.evidence_chain: supporting = sum(1 for e in claim.evidence_chain if not e.contradictory) contradicting = sum(1 for e in claim.evidence_chain if e.contradictory) if supporting > 0 and contradicting == 0: biases_detected.append("potential_confirmation_bias") # Availability bias (recent evidence weighted more) recent_evidence = sum(1 for e in claim.evidence_chain if (datetime.utcnow() - e.timestamp).days < 30) if recent_evidence / max(1, len(claim.evidence_chain)) > 0.8: biases_detected.append("potential_availability_bias") # Calculate overall quality complexity_factor = len(claim.sub_domains) / max(1, len(KnowledgeDomain)) reasoning_diversity = len(claim.reasoning_modes) / max(1, len(ReasoningMode)) overall_quality = ( 0.4 * (1.0 - len(biases_detected) / 5) + # Bias penalty 0.3 * complexity_factor + # Domain complexity 0.3 * reasoning_diversity # Reasoning diversity ) return { 'overall_quality': max(0.0, min(1.0, overall_quality)), 'detected_biases': biases_detected, 'bias_score': len(biases_detected) / 5, 'complexity_factor': complexity_factor, 'reasoning_diversity': reasoning_diversity, 'processing_time': time.monotonic() - start_time } except Exception as e: logger.error(f"Metacognitive assessment failed: {str(e)}") return { 'overall_quality': 0.5, 'detected_biases': [], 'error': str(e), 'processing_time': time.monotonic() - start_time } # -------------------------- # COMPLEXITY ANALYTICS COMPONENT # -------------------------- async def _analyze_claim_complexity(self, claim: UniversalClaim) -> Dict: """Enhanced complexity analysis""" start_time = time.monotonic() try: # Text complexity (simplified) content_length = len(claim.content) word_count = len(claim.content.split()) # Domain complexity domain_complexity = len(claim.sub_domains) / len(KnowledgeDomain) # Evidence complexity evidence_complexity = len(claim.evidence_chain) / 10 # Normalized to 10 pieces # Reasoning complexity reasoning_complexity = len(claim.reasoning_modes) / len(ReasoningMode) # Causal complexity causal_complexity = len(claim.causal_mechanisms) / 5 # Normalized to 5 mechanisms # Overall complexity overall_complexity = np.mean([ min(1.0, content_length / 1000), # Text length factor domain_complexity, evidence_complexity, reasoning_complexity, causal_complexity ]) return { 'overall_complexity': overall_complexity, 'complexity_factors': { 'content_length': content_length, 'word_count': word_count, 'domain_complexity': domain_complexity, 'evidence_complexity': evidence_complexity, 'reasoning_complexity': reasoning_complexity, 'causal_complexity': causal_complexity }, 'processing_time': time.monotonic() - start_time } except Exception as e: logger.error(f"Complexity analysis failed: {str(e)}") return { 'overall_complexity': 0.5, 'error': str(e), 'processing_time': time.monotonic() - start_time } # -------------------------- # CROSS-DOMAIN ANALYTICS COMPONENT # -------------------------- def _assess_cross_domain_coherence(self, claim: UniversalClaim) -> float: """Assess coherence across knowledge domains""" try: if len(claim.sub_domains) <= 1: return 0.8 # Single domain claims are generally coherent # Known conflicting domain pairs conflicting_pairs = [ (KnowledgeDomain.SCIENCE, KnowledgeDomain.PHILOSOPHY), (KnowledgeDomain.SCIENCE, KnowledgeDomain.HISTORY), (KnowledgeDomain.MEDICINE, KnowledgeDomain.PHILOSOPHY) ] # Check for domain conflicts domain_set = set(claim.sub_domains) conflict_count = 0 for pair in conflicting_pairs: if pair[0] in domain_set and pair[1] in domain_set: conflict_count += 1 # Domain diversity bonus domain_diversity = len(domain_set) / len(KnowledgeDomain) # Calculate coherence score base_coherence = 0.7 conflict_penalty = conflict_count * 0.15 diversity_bonus = domain_diversity * 0.1 return max(0.3, min(0.95, base_coherence - conflict_penalty + diversity_bonus)) except Exception as e: logger.error(f"Cross-domain coherence assessment failed: {str(e)}") return 0.5 # -------------------------- # VALIDATION CORE COMPONENT # -------------------------- def _calculate_overall_validity(self, components: Dict, mcp_results: Dict) -> float: """Calculate comprehensive overall validity score""" try: weights = { 'reasoning': 0.25, 'evidence': 0.25, 'metacognitive': 0.15, 'cross_domain': 0.1, 'complexity': 0.05, 'mcp_consensus': 0.2 } # Extract component scores reasoning_score = components['reasoning_results'].get('bayesian', {}).get('posterior', 0.5) or \ components['reasoning_results'].get('causal', {}).get('causal_coherence', 0.5) or 0.5 evidence_score = components['evidence_analysis'].get('evidence_score', 0.5) meta_score = components['metacognitive_assessment'].get('overall_quality', 0.5) cross_domain_score = components['cross_domain_coherence'] complexity_score = 0.5 # Complexity doesn't directly affect validity # Apply mcp consensus with reliability weighting mcp_score = mcp_results.get('consensus_score', 0.5) mcp_reliability = mcp_results.get('reliability', 0.0) adjusted_mcp = mcp_reliability * mcp_score + (1 - mcp_reliability) * 0.5 # Calculate weighted sum weighted_sum = ( weights['reasoning'] * reasoning_score + weights['evidence'] * evidence_score + weights['metacognitive'] * meta_score + weights['cross_domain'] * cross_domain_score + weights['complexity'] * complexity_score + weights['mcp_consensus'] * adjusted_mcp ) # Apply bias penalty bias_penalty = min(0.15, len(components['metacognitive_assessment'].get('detected_biases', [])) * 0.05) final_score = max(0.0, min(1.0, weighted_sum - bias_penalty)) return final_score except Exception as e: logger.error(f"Validity calculation failed: {str(e)}") return 0.5 def _calculate_confidence_intervals(self, validity_score: float, evidence_count: int) -> Dict: """Calculate confidence intervals based on validity score and evidence""" try: # Base interval range based on evidence count if evidence_count == 0: base_range = 0.4 elif evidence_count < 3: base_range = 0.3 elif evidence_count < 5: base_range = 0.2 elif evidence_count < 10: base_range = 0.15 else: base_range = 0.1 # Adjust based on score (higher scores have tighter intervals) range_adjustment = (1 - validity_score) * 0.1 final_range = max(0.05, min(0.4, base_range + range_adjustment)) lower_bound = max(0.0, validity_score - final_range/2) upper_bound = min(1.0, validity_score + final_range/2) return { "lower_bound": lower_bound, "upper_bound": upper_bound, "range": final_range, "evidence_count": evidence_count } except Exception as e: logger.error(f"Confidence interval calculation failed: {str(e)}") return { "lower_bound": max(0.0, validity_score - 0.2), "upper_bound": min(1.0, validity_score + 0.2), "range": 0.4, "error": str(e) } def _generate_enhancement_recommendations(self, claim: UniversalClaim, results: Dict) -> List[str]: """Generate intelligent enhancement recommendations""" recommendations = [] # Evidence-related recommendations evidence_analysis = results.get('evidence_analysis', {}) if evidence_analysis.get('evidence_count', 0) < 3: recommendations.append("Add more supporting evidence from diverse sources") if evidence_analysis.get('quality_factors', {}).get('contradiction_penalty', 0) > 0.1: recommendations.append("Address contradictory evidence or explain inconsistencies") if evidence_analysis.get('quality_factors', {}).get('domain_diversity', 0) < 0.3: recommendations.append("Include evidence from additional knowledge domains") # Reasoning-related recommendations reasoning_modes = claim.reasoning_modes if ReasoningMode.BAYESIAN not in reasoning_modes and evidence_analysis.get('evidence_count', 0) > 2: recommendations.append("Consider applying Bayesian reasoning to quantify evidence strength") if ReasoningMode.CAUSAL not in reasoning_modes and claim.causal_mechanisms: recommendations.append("Apply causal reasoning to better articulate causal mechanisms") # Metacognitive recommendations meta = results.get('metacognitive_assessment', {}) if 'potential_confirmation_bias' in meta.get('detected_biases', []): recommendations.append("Actively seek contradictory evidence to avoid confirmation bias") if 'potential_availability_bias' in meta.get('detected_biases', []): recommendations.append("Include historical evidence to counter recent evidence bias") # Complexity recommendations complexity = results.get('complexity_analysis', {}) if complexity.get('overall_complexity', 0) > 0.7: recommendations.append("Break down into simpler sub-claims for better validation") return recommendations def _store_validation_result(self, claim_id: str, report: Dict): """Store validation result in history""" entry = { "claim_id": claim_id, "timestamp": datetime.utcnow(), "report": report } self.validation_history.append(entry) # Maintain history size if len(self.validation_history) > self.max_history: self.validation_history.pop(0) def _get_system_load(self) -> Dict: """Get current system performance metrics""" try: return { "cpu_percent": psutil.cpu_percent(), "memory_percent": psutil.virtual_memory().percent, "disk_percent": psutil.disk_usage('/').percent, "process_memory": psutil.Process().memory_info().rss / (1024 * 1024) # in MB } except Exception as e: logger.warning(f"Could not get system load: {str(e)}") return {"error": str(e)} async def validate_knowledge_claim(self, claim: UniversalClaim) -> Dict: """Comprehensive claim validation pipeline""" validation_start = time.monotonic() report = {"claim_id": claim.claim_id} try: # Execute validation components in parallel mcp_task = asyncio.create_task(self._get_mcp_consensus(claim)) reasoning_task = asyncio.create_task(self._perform_reasoning_analysis(claim)) evidence_task = asyncio.create_task(self._analyze_evidence_quality(claim)) meta_task = asyncio.create_task(self._metacognitive_assessment(claim)) complexity_task = asyncio.create_task(self._analyze_claim_complexity(claim)) # Gather results mcp_results, reasoning_results, evidence_analysis, meta_assessment, complexity_analysis = await asyncio.gather( mcp_task, reasoning_task, evidence_task, meta_task, complexity_task ) # Assess cross-domain coherence cross_domain_coherence = self._assess_cross_domain_coherence(claim) # Build intermediate report report = { "mcp_consensus": mcp_results, "reasoning_analysis": reasoning_results, "evidence_analysis": evidence_analysis, "metacognitive_assessment": meta_assessment, "cross_domain_coherence": cross_domain_coherence, "complexity_analysis": complexity_analysis } # Calculate overall validity overall_validity = self._calculate_overall_validity( { 'reasoning_results': reasoning_results, 'evidence_analysis': evidence_analysis, 'metacognitive_assessment': meta_assessment, 'cross_domain_coherence': cross_domain_coherence, 'complexity_analysis': complexity_analysis }, mcp_results ) # Calculate confidence intervals evidence_count = evidence_analysis.get('evidence_count', 0) confidence_intervals = self._calculate_confidence_intervals(overall_validity, evidence_count) # Generate recommendations all_validation_results = { 'reasoning_results': reasoning_results, 'evidence_analysis': evidence_analysis, 'metacognitive_assessment': meta_assessment, 'complexity_analysis': complexity_analysis } recommendations = self._generate_enhancement_recommendations(claim, all_validation_results) # System metrics total_processing_time = time.monotonic() - validation_start system_load = self._get_system_load() # Build comprehensive report report.update({ "claim": claim.to_dict(), "overall_validity": overall_validity, "confidence_intervals": confidence_intervals, "validation_components": { "reasoning_analysis": reasoning_results, "evidence_analysis": evidence_analysis, "metacognitive_assessment": meta_assessment, "complexity_analysis": complexity_analysis, "cross_domain_coherence": cross_domain_coherence, "mcp_consensus": mcp_results }, "enhancement_recommendations": recommendations, "system_metrics": { "total_processing_time": total_processing_time, "system_load": system_load, "validation_timestamp": datetime.utcnow().isoformat(), "cache_hits": 1 if mcp_results.get('cache_hit') else 0 }, "validation_metadata": { "validator_version": "2.0.0", "reasoning_modes_used": [m.value for m in claim.reasoning_modes], "domains_analyzed": [d.value for d in claim.sub_domains], "evidence_sources": len(claim.evidence_chain) } }) # Determine final status if overall_validity >= 0.8: report["status"] = ValidationStatus.SUCCESS.value elif overall_validity >= 0.6: report["status"] = ValidationStatus.PARTIAL_SUCCESS.value else: report["status"] = ValidationStatus.FAILURE.value # Store result self._store_validation_result(claim.claim_id, report) logger.info(f"Validation completed for {claim.claim_id} in {total_processing_time:.2f}s with score {overall_validity:.3f}") except Exception as e: logger.exception(f"Critical error in validation: {str(e)}") report.update({ "status": ValidationStatus.ERROR.value, "error": str(e), "partial_results": locals().get('validation_results', {}), "processing_time": time.monotonic() - validation_start }) return report # -------------------------- # TESTING COMPONENT # -------------------------- def _initialize_test_cases(self) -> List[UniversalClaim]: """Initialize comprehensive test cases for validation""" test_cases = [] # Scientific claim with strong evidence science_evidence = [ Evidence("sci_001", 0.9, 0.95, domain=KnowledgeDomain.SCIENCE), Evidence("sci_002", 0.85, 0.9, domain=KnowledgeDomain.SCIENCE), Evidence("sci_003", 0.8, 0.88, domain=KnowledgeDomain.MATHEMATICS) ] science_claim = UniversalClaim( claim_id="test_science_001", content="The speed of light in vacuum is approximately 299,792,458 meters per second", evidence_chain=science_evidence, reasoning_modes=[ReasoningMode.DEDUCTIVE, ReasoningMode.BAYESIAN], sub_domains=[KnowledgeDomain.SCIENCE, KnowledgeDomain.MATHEMATICS], causal_mechanisms=["electromagnetic_wave_propagation", "spacetime_geometry"], expected_validity=0.95 ) test_cases.append(science_claim) # Philosophical claim with mixed evidence philosophy_evidence = [ Evidence("phil_001", 0.6, 0.7, domain=KnowledgeDomain.PHILOSOPHY), Evidence("phil_002", 0.4, 0.6, contradictory=True, domain=KnowledgeDomain.PHILOSOPHY), Evidence("phil_003", 0.7, 0.75, domain=KnowledgeDomain.SOCIAL_SCIENCE) ] philosophy_claim = UniversalClaim( claim_id="test_philosophy_001", content="Free will is incompatible with determinism in all possible worlds", evidence_chain=philosophy_evidence, reasoning_modes=[ReasoningMode.DEDUCTIVE, ReasoningMode.ABDUCTIVE], sub_domains=[KnowledgeDomain.PHILOSOPHY, KnowledgeDomain.SOCIAL_SCIENCE], causal_mechanisms=["deterministic_causation", "agent_causation"], expected_validity=0.65 ) test_cases.append(philosophy_claim) # Medical claim with recent evidence medical_evidence = [ Evidence("med_001", 0.85, 0.9, domain=KnowledgeDomain.MEDICINE), Evidence("med_002", 0.8, 0.85, domain=KnowledgeDomain.SCIENCE), Evidence("med_003", 0.75, 0.8, domain=KnowledgeDomain.MEDICINE, timestamp=datetime.utcnow() - timedelta(days=10)) ] medical_claim = UniversalClaim( claim_id="test_medical_001", content="Regular exercise reduces the risk of cardiovascular disease by approximately 30-35%", evidence_chain=medical_evidence, reasoning_modes=[ReasoningMode.BAYESIAN, ReasoningMode.CAUSAL], sub_domains=[KnowledgeDomain.MEDICINE, KnowledgeDomain.SCIENCE], causal_mechanisms=["improved_cardiac_output", "reduced_inflammation", "weight_management"], expected_validity=0.8 ) test_cases.append(medical_claim) return test_cases async def run_validation_tests(self) -> Dict: """Run comprehensive validation tests""" logger.info("Starting comprehensive validation tests") test_start = time.monotonic() results = { "test_summary": { "total_tests": len(self.test_cases), "passed": 0, "failed": 0, "errors": 0 }, "detailed_results": [], "performance_metrics": {} } for test_case in self.test_cases: try: logger.info(f"Testing claim: {test_case.claim_id}") validation_result = await self.validate_knowledge_claim(test_case) # Check if result matches expected validity actual_validity = validation_result.get("overall_validity", 0.0) expected_validity = test_case.expected_validity or 0.5 # Allow 15% tolerance tolerance = 0.15 passed = abs(actual_validity - expected_validity) <= tolerance test_result = { "claim_id": test_case.claim_id, "expected_validity": expected_validity, "actual_validity": actual_validity, "difference": abs(actual_validity - expected_validity), "passed": passed, "status": validation_result.get("status"), "processing_time": validation_result.get("system_metrics", {}).get("total_processing_time", 0), "recommendations_count": len(validation_result.get("enhancement_recommendations", [])) } results["detailed_results"].append(test_result) if validation_result.get("status") == ValidationStatus.ERROR.value: results["test_summary"]["errors"] += 1 elif passed: results["test_summary"]["passed"] += 1 else: results["test_summary"]["failed"] += 1 except Exception as e: logger.error(f"Test failed for {test_case.claim_id}: {str(e)}") results["test_summary"]["errors"] += 1 results["detailed_results"].append({ "claim_id": test_case.claim_id, "error": str(e), "passed": False }) total_test_time = time.monotonic() - test_start results["performance_metrics"] = { "total_test_time": total_test_time, "average_test_time": total_test_time / len(self.test_cases), "tests_per_second": len(self.test_cases) / total_test_time if total_test_time > 0 else 0, "cache_hit_rate": len([r for r in results["detailed_results"] if "cache_hit" in str(r)]) / len(self.test_cases) } logger.info(f"Validation tests completed in {total_test_time:.2f}s") logger.info(f"Results: {results['test_summary']['passed']} passed, " f"{results['test_summary']['failed']} failed, " f"{results['test_summary']['errors']} errors") return results # -------------------------- # ANALYTICS COMPONENT # -------------------------- def get_validation_statistics(self) -> Dict: """Get comprehensive validation statistics""" if not self.validation_history: return {"message": "No validation history available"} try: # Extract validity scores validity_scores = [] processing_times = [] statuses = [] for entry in self.validation_history: report = entry.get("report", {}) if "overall_validity" in report: validity_scores.append(report["overall_validity"]) if "system_metrics" in report: processing_times.append( report["system_metrics"].get("total_processing_time", 0) ) statuses.append(report.get("status", "unknown")) # Calculate statistics stats = { "total_validations": len(self.validation_history), "validity_statistics": { "mean": np.mean(validity_scores) if validity_scores else 0, "median": np.median(validity_scores) if validity_scores else 0, "std_dev": np.std(validity_scores) if validity_scores else 0, "min": np.min(validity_scores) if validity_scores else 0, "max": np.max(validity_scores) if validity_scores else 0 }, "performance_statistics": { "mean_processing_time": np.mean(processing_times) if processing_times else 0, "median_processing_time": np.median(processing_times) if processing_times else 0, "total_processing_time": np.sum(processing_times) if processing_times else 0 }, "status_distribution": { status: statuses.count(status) for status in set(statuses) }, "cache_statistics": { "cache_size": len(self.validation_cache), "cache_hit_rate": len([r for r in self.validation_history if r.get("report", {}).get("validation_components", {}) .get("mcp_consensus", {}).get("cache_hit")]) / len(self.validation_history) } } return stats except Exception as e: logger.error(f"Error calculating statistics: {str(e)}") return {"error": str(e)} # -------------------------- # DATA EXPORT COMPONENT # -------------------------- def export_validation_history(self, format: str = "json") -> str: """Export validation history in specified format""" try: if format.lower() == "json": return json.dumps(self.validation_history, indent=2, default=str) elif format.lower() == "csv": # Convert to CSV-friendly format csv_data = [] for entry in self.validation_history: report = entry.get("report", {}) csv_row = { "claim_id": entry.get("claim_id", ""), "timestamp": entry.get("timestamp", ""), "overall_validity": report.get("overall_validity", 0), "status": report.get("status", ""), "processing_time": report.get("system_metrics", {}).get("total_processing_time", 0), "evidence_count": report.get("claim", {}).get("evidence_summary", {}).get("count", 0) } csv_data.append(csv_row) if csv_data: import csv import io output = io.StringIO() writer = csv.DictWriter(output, fieldnames=csv_data[0].keys()) writer.writeheader() writer.writerows(csv_data) return output.getvalue() else: return "No validation history to export" else: return f"Unsupported format: {format}. Use 'json' or 'csv'" except Exception as e: logger.error(f"Error exporting validation history: {str(e)}") return f"Export error: {str(e)}" # -------------------------- # MAINTENANCE COMPONENT # -------------------------- def clear_cache(self): """Clear validation cache""" self.validation_cache.clear() logger.info("Validation cache cleared") def clear_history(self): """Clear validation history""" self.validation_history.clear() logger.info("Validation history cleared") # -------------------------- # MAIN EXECUTION COMPONENT # -------------------------- async def main(): """Enhanced main function with comprehensive testing""" # Initialize the validator agi_validator = AdvancedGeneralIntelligence( mcp_enabled=True, mcp_timeout=15, max_history=100, cache_enabled=True ) try: # Run comprehensive tests print("Running comprehensive validation tests...") test_results = await agi_validator.run_validation_tests() print(f"\nTest Results Summary:") print(f"Total Tests: {test_results['test_summary']['total_tests']}") print(f"Passed: {test_results['test_summary']['passed']}") print(f"Failed: {test_results['test_summary']['failed']}") print(f"Errors: {test_results['test_summary']['errors']}") print(f"Average Processing Time: {test_results['performance_metrics']['average_test_time']:.3f}s") # Create a custom claim for validation custom_evidence = [ Evidence("custom_001", 0.85, 0.9, domain=KnowledgeDomain.TECHNOLOGY), Evidence("custom_002", 0.8, 0.85, domain=KnowledgeDomain.SCIENCE), Evidence("custom_003", 0.75, 0.8, domain=KnowledgeDomain.SOCIAL_SCIENCE) ] custom_claim = UniversalClaim( claim_id="custom_ai_claim", content="Artificial General Intelligence will be achieved within the next decade through scaling transformer architectures", evidence_chain=custom_evidence, reasoning_modes=[ReasoningMode.BAYESIAN, ReasoningMode.CAUSAL, ReasoningMode.INDUCTIVE], sub_domains=[KnowledgeDomain.TECHNOLOGY, KnowledgeDomain.SCIENCE, KnowledgeDomain.SOCIAL_SCIENCE], causal_mechanisms=["computational_scaling", "architectural_improvements", "data_availability"], expected_validity=0.7 ) print(f"\nValidating custom claim: {custom_claim.content[:50]}...") custom_result = await agi_validator.validate_knowledge_claim(custom_claim) print(f"Validation Result:") print(f"Overall Validity: {custom_result['overall_validity']:.3f}") print(f"Status: {custom_result['status']}") print(f"Confidence Interval: {custom_result['confidence_intervals']}") print(f"Processing Time: {custom_result['system_metrics']['total_processing_time']:.3f}s") print(f"\nEnhancement Recommendations:") for i, rec in enumerate(custom_result['enhancement_recommendations'], 1): print(f"{i}. {rec}") # Get validation statistics stats = agi_validator.get_validation_statistics() print(f"\nValidation Statistics:") print(f"Total Validations: {stats['total_validations']}") print(f"Mean Validity Score: {stats['validity_statistics']['mean']:.3f}") print(f"Mean Processing Time: {stats['performance_statistics']['mean_processing_time']:.3f}s") except Exception as e: logger.exception(f"Error in main execution: {str(e)}") finally: # Clean up resources await agi_validator.close() if __name__ == "__main__": asyncio.run(main())