""" Advanced Cognitive Integration System for Phase 9 Components Orchestrates all cognitive systems for unified intelligent operation """ import asyncio import sqlite3 import json import uuid from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, asdict import logging from pathlib import Path import threading import time logger = logging.getLogger(__name__) # Import all Phase 9 cognitive systems from .long_term_memory import LongTermMemoryManager from .episodic_memory import EpisodicMemorySystem from .semantic_memory import SemanticMemoryNetwork from .working_memory import WorkingMemoryManager from .chain_of_thought import ChainOfThoughtReasoning # Try to import meta-cognitive monitor, fall back to None if torch not available try: from .meta_cognitive import MetaCognitiveMonitor except ImportError as e: logger.warning(f"Meta-cognitive monitor not available (torch dependency): {e}") MetaCognitiveMonitor = None @dataclass class CognitiveState: """Current state of the integrated cognitive system""" timestamp: datetime working_memory_load: float attention_focus: Optional[str] reasoning_quality: float learning_rate: float confidence_level: float cognitive_load: float active_episodes: int memory_consolidation_status: str class AdvancedCognitiveSystem: """Unified cognitive system integrating all Phase 9 components""" def __init__(self, base_path: str = "data/cognitive"): """Initialize the integrated cognitive system""" self.base_path = Path(base_path) self.base_path.mkdir(parents=True, exist_ok=True) # Initialize all cognitive subsystems self._init_cognitive_subsystems() # Integration state self.current_state = None self.integration_active = True # Background processes self._consolidation_thread = None self._monitoring_thread = None # Start integrated operation self._start_cognitive_integration() logger.info("Advanced Cognitive System initialized with full Phase 9 integration") def _init_cognitive_subsystems(self): """Initialize all cognitive subsystems""" try: # Memory systems self.long_term_memory = LongTermMemoryManager( db_path=self.base_path / "long_term_memory.db" ) self.episodic_memory = EpisodicMemorySystem( db_path=self.base_path / "episodic_memory.db" ) self.semantic_memory = SemanticMemoryNetwork( db_path=self.base_path / "semantic_memory.db" ) self.working_memory = WorkingMemoryManager( db_path=self.base_path / "working_memory.db" ) # Reasoning systems self.chain_of_thought = ChainOfThoughtReasoning( db_path=self.base_path / "reasoning_chains.db" ) # Meta-cognitive monitoring (optional if torch available) if MetaCognitiveMonitor is not None: self.meta_cognitive = MetaCognitiveMonitor( db_path=self.base_path / "metacognitive.db" ) logger.info("Meta-cognitive monitoring enabled") else: self.meta_cognitive = None logger.info("Meta-cognitive monitoring disabled (torch not available)") logger.info("All cognitive subsystems initialized successfully") except Exception as e: logger.error(f"Error initializing cognitive subsystems: {e}") raise def _start_cognitive_integration(self): """Start background processes for cognitive integration""" try: # Start memory consolidation thread self._consolidation_thread = threading.Thread( target=self._memory_consolidation_loop, daemon=True ) self._consolidation_thread.start() # Start cognitive monitoring thread self._monitoring_thread = threading.Thread( target=self._cognitive_monitoring_loop, daemon=True ) self._monitoring_thread.start() logger.info("Cognitive integration processes started") except Exception as e: logger.error(f"Error starting cognitive integration: {e}") async def process_agent_experience(self, agent_id: str, experience_data: Dict[str, Any]) -> Dict[str, Any]: """Process a complete agent experience through all cognitive systems""" try: processing_id = str(uuid.uuid4()) # Start episode in episodic memory episode_id = self.episodic_memory.start_episode( agent_id=agent_id, session_id=experience_data.get('session_id', ''), episode_type=experience_data.get('type', 'operation'), context=experience_data.get('context', {}) ) # Add to working memory for immediate processing wm_item_id = self.working_memory.add_item( content=f"Processing experience: {experience_data.get('description', 'Unknown')}", item_type="experience", priority=experience_data.get('priority', 0.7), source_agent=agent_id, context_tags=experience_data.get('tags', []) ) # Extract semantic concepts for knowledge graph concepts_added = [] if 'indicators' in experience_data: for indicator in experience_data['indicators']: concept_id = self.semantic_memory.add_concept( name=indicator, concept_type=experience_data.get('indicator_type', 'unknown'), description=f"Observed in agent {agent_id} experience", confidence=0.7, source=f"agent_{agent_id}" ) if concept_id: concepts_added.append(concept_id) # Perform reasoning about the experience reasoning_result = None if experience_data.get('requires_reasoning', True): threat_indicators = experience_data.get('indicators', []) if threat_indicators: reasoning_result = await asyncio.to_thread( self.chain_of_thought.reason_about_threat, threat_indicators, agent_id ) # Record experience steps in episodic memory for action in experience_data.get('actions', []): self.episodic_memory.record_action(episode_id, action) for observation in experience_data.get('observations', []): self.episodic_memory.record_observation(episode_id, observation) # Calculate reward based on success reward = 1.0 if experience_data.get('success', False) else 0.3 self.episodic_memory.record_reward(episode_id, reward) # Complete episode self.episodic_memory.end_episode( episode_id=episode_id, success=experience_data.get('success', False), outcome=experience_data.get('outcome', ''), metadata={'processing_id': processing_id} ) # Store significant experiences in long-term memory if experience_data.get('importance', 0.5) > 0.6: ltm_id = self.long_term_memory.store_memory( content=f"Significant experience: {experience_data.get('description')}", memory_type="episodic_significant", importance=experience_data.get('importance', 0.7), agent_id=agent_id, tags=experience_data.get('tags', []) ) # Record performance metrics for meta-cognitive monitoring if reasoning_result and self.meta_cognitive: self.meta_cognitive.record_performance_metric( metric_name="reasoning_confidence", metric_type="reasoning", value=reasoning_result.get('threat_assessment', {}).get('confidence', 0.5), agent_id=agent_id ) # Generate processing result result = { 'processing_id': processing_id, 'episode_id': episode_id, 'working_memory_item_id': wm_item_id, 'concepts_added': len(concepts_added), 'reasoning_performed': reasoning_result is not None, 'reasoning_result': reasoning_result, 'cognitive_state': await self._get_current_cognitive_state(agent_id), 'recommendations': await self._generate_integrated_recommendations( experience_data, reasoning_result, agent_id ) } logger.info(f"Agent experience processed through all cognitive systems: {processing_id}") return result except Exception as e: logger.error(f"Error processing agent experience: {e}") return {'error': str(e)} async def perform_integrated_threat_analysis(self, threat_indicators: List[str], agent_id: str = "") -> Dict[str, Any]: """Perform comprehensive threat analysis using all cognitive systems""" try: analysis_id = str(uuid.uuid4()) # Retrieve relevant memories from long-term memory relevant_memories = self.long_term_memory.retrieve_memories( query=' '.join(threat_indicators[:3]), memory_type="", agent_id=agent_id, limit=10 ) # Get related concepts from semantic memory semantic_reasoning = self.semantic_memory.reason_about_threat(threat_indicators) # Perform chain-of-thought reasoning cot_reasoning = await asyncio.to_thread( self.chain_of_thought.reason_about_threat, threat_indicators, agent_id ) # Find similar past episodes similar_episodes = [] for indicator in threat_indicators[:3]: episodes = self.episodic_memory.get_episodes_for_replay( agent_id=agent_id, episode_type="", success_only=False, limit=5 ) for episode in episodes: if any(indicator.lower() in action.get('content', '').lower() for action in episode.actions): similar_episodes.append(episode) # Add to working memory for focused attention wm_item_id = self.working_memory.add_item( content=f"Threat analysis: {', '.join(threat_indicators[:3])}", item_type="threat_analysis", priority=0.9, source_agent=agent_id, context_tags=["threat", "analysis", "high_priority"] ) # Focus attention on threat analysis focus_id = self.working_memory.focus_attention( focus_type="threat_analysis", item_ids=[wm_item_id], attention_weight=0.9, agent_id=agent_id ) # Synthesize results from all systems integrated_assessment = await self._synthesize_threat_assessment( semantic_reasoning, cot_reasoning, relevant_memories, similar_episodes ) # Generate comprehensive recommendations recommendations = await self._generate_comprehensive_recommendations( integrated_assessment, threat_indicators ) # Record analysis for meta-cognitive learning if self.meta_cognitive: self.meta_cognitive.record_performance_metric( metric_name="integrated_threat_analysis", metric_type="analysis", value=integrated_assessment['confidence'], target_value=0.8, context={ 'analysis_id': analysis_id, 'indicators_count': len(threat_indicators), 'memories_used': len(relevant_memories) }, agent_id=agent_id ) result = { 'analysis_id': analysis_id, 'threat_indicators': threat_indicators, 'integrated_assessment': integrated_assessment, 'recommendations': recommendations, 'supporting_evidence': { 'semantic_reasoning': semantic_reasoning, 'cot_reasoning': cot_reasoning, 'relevant_memories': len(relevant_memories), 'similar_episodes': len(similar_episodes) }, 'cognitive_resources_used': { 'working_memory_item': wm_item_id, 'attention_focus': focus_id, 'reasoning_chains': cot_reasoning.get('chain_id', ''), 'semantic_concepts': len(semantic_reasoning.get('matched_concepts', [])) } } logger.info(f"Integrated threat analysis completed: {analysis_id}") return result except Exception as e: logger.error(f"Error in integrated threat analysis: {e}") return {'error': str(e)} async def trigger_cognitive_reflection(self, agent_id: str, trigger_event: str = "periodic") -> Dict[str, Any]: """Trigger comprehensive cognitive reflection across all systems""" try: reflection_id = str(uuid.uuid4()) # Perform meta-cognitive reflection if available meta_reflection = None if self.meta_cognitive: meta_reflection = await asyncio.to_thread( self.meta_cognitive.trigger_self_reflection, agent_id, trigger_event, "comprehensive" ) # Get cross-session context from long-term memory cross_session_memories = self.long_term_memory.get_cross_session_context( agent_id=agent_id, limit=15 ) # Discover patterns in episodic memory episode_patterns = await asyncio.to_thread( self.episodic_memory.discover_patterns ) # Consolidate memories consolidation_stats = await asyncio.to_thread( self.long_term_memory.consolidate_memories ) # Assess working memory efficiency wm_stats = self.working_memory.get_working_memory_statistics() # Generate reflection insights reflection_insights = await self._generate_reflection_insights( meta_reflection, cross_session_memories, episode_patterns, consolidation_stats, wm_stats ) # Update cognitive state new_state = await self._update_cognitive_state_from_reflection( agent_id, reflection_insights ) result = { 'reflection_id': reflection_id, 'trigger_event': trigger_event, 'agent_id': agent_id, 'meta_reflection': meta_reflection, 'reflection_insights': reflection_insights, 'cognitive_state_update': new_state, 'system_optimizations': await self._apply_reflection_optimizations( reflection_insights, agent_id ), 'learning_adjustments': await self._apply_learning_adjustments( meta_reflection, agent_id ) } logger.info(f"Comprehensive cognitive reflection completed: {reflection_id}") return result except Exception as e: logger.error(f"Error in cognitive reflection: {e}") return {'error': str(e)} def _memory_consolidation_loop(self): """Background memory consolidation process""" consolidation_interval = 21600 # 6 hours while self.integration_active: try: time.sleep(consolidation_interval) # Consolidate long-term memory ltm_stats = self.long_term_memory.consolidate_memories() # Discover patterns in episodic memory pattern_stats = self.episodic_memory.discover_patterns() # Decay working memory self.working_memory.decay_memory() logger.info(f"Memory consolidation completed - LTM: {ltm_stats.get('patterns_discovered', 0)} patterns, Episodes: {len(pattern_stats.get('action_patterns', []))} action patterns") except Exception as e: logger.error(f"Error in memory consolidation loop: {e}") def _cognitive_monitoring_loop(self): """Background cognitive monitoring process""" monitoring_interval = 300 # 5 minutes while self.integration_active: try: time.sleep(monitoring_interval) # Update current cognitive state self.current_state = self._calculate_integrated_cognitive_state() # Check for cognitive load issues if self.current_state.cognitive_load > 0.8: logger.warning(f"High cognitive load detected: {self.current_state.cognitive_load:.3f}") # Monitor working memory capacity if self.current_state.working_memory_load > 0.9: logger.warning(f"Working memory near capacity: {self.current_state.working_memory_load:.3f}") except Exception as e: logger.error(f"Error in cognitive monitoring loop: {e}") def _calculate_integrated_cognitive_state(self) -> CognitiveState: """Calculate current integrated cognitive state""" try: # Get statistics from all subsystems wm_stats = self.working_memory.get_working_memory_statistics() ltm_stats = self.long_term_memory.get_memory_statistics() episodic_stats = self.episodic_memory.get_episodic_statistics() reasoning_stats = self.chain_of_thought.get_reasoning_statistics() # Calculate working memory load wm_load = wm_stats.get('utilization', 0.0) # Get current attention focus current_focus = self.working_memory.get_current_focus() focus_type = current_focus.focus_type if current_focus else None # Calculate reasoning quality from recent chains reasoning_quality = 0.7 # Default if reasoning_stats.get('total_chains', 0) > 0: completion_rate = reasoning_stats.get('completion_rate', 0.5) avg_confidence = 0.6 # Would calculate from actual data reasoning_quality = (completion_rate + avg_confidence) / 2 # Estimate cognitive load task_count = wm_stats.get('current_capacity', 0) cognitive_load = min(task_count / 50.0 + wm_load * 0.3, 1.0) return CognitiveState( timestamp=datetime.now(), working_memory_load=wm_load, attention_focus=focus_type, reasoning_quality=reasoning_quality, learning_rate=0.01, # Would be calculated dynamically confidence_level=0.75, # Would be calculated from meta-cognitive data cognitive_load=cognitive_load, active_episodes=len(self.episodic_memory._active_episodes), memory_consolidation_status="active" ) except Exception as e: logger.error(f"Error calculating cognitive state: {e}") return CognitiveState( timestamp=datetime.now(), working_memory_load=0.5, attention_focus=None, reasoning_quality=0.5, learning_rate=0.01, confidence_level=0.5, cognitive_load=0.5, active_episodes=0, memory_consolidation_status="error" ) async def _get_current_cognitive_state(self, agent_id: str) -> Dict[str, Any]: """Get current cognitive state for specific agent""" state = self._calculate_integrated_cognitive_state() return asdict(state) async def _synthesize_threat_assessment(self, semantic_result: Dict[str, Any], cot_result: Dict[str, Any], memories: List[Any], episodes: List[Any]) -> Dict[str, Any]: """Synthesize threat assessment from all cognitive systems""" # Extract confidence levels semantic_confidence = semantic_result.get('confidence', 0.5) cot_confidence = cot_result.get('threat_assessment', {}).get('confidence', 0.5) # Weight based on evidence availability semantic_weight = 0.3 cot_weight = 0.4 memory_weight = 0.2 episode_weight = 0.1 # Memory contribution memory_confidence = min(len(memories) / 5.0, 1.0) * 0.7 episode_confidence = min(len(episodes) / 3.0, 1.0) * 0.6 # Weighted confidence overall_confidence = ( semantic_confidence * semantic_weight + cot_confidence * cot_weight + memory_confidence * memory_weight + episode_confidence * episode_weight ) # Determine threat level if overall_confidence > 0.8: threat_level = "CRITICAL" elif overall_confidence > 0.6: threat_level = "HIGH" elif overall_confidence > 0.4: threat_level = "MEDIUM" else: threat_level = "LOW" return { 'threat_level': threat_level, 'confidence': overall_confidence, 'evidence_sources': { 'semantic_analysis': semantic_confidence, 'reasoning_chains': cot_confidence, 'historical_memories': memory_confidence, 'similar_episodes': episode_confidence }, 'synthesis_method': 'integrated_weighted_assessment' } async def _generate_integrated_recommendations(self, experience_data: Dict[str, Any], reasoning_result: Optional[Dict[str, Any]], agent_id: str) -> List[Dict[str, Any]]: """Generate recommendations based on integrated cognitive analysis""" recommendations = [] # Based on experience importance if experience_data.get('importance', 0.5) > 0.8: recommendations.append({ 'type': 'memory_consolidation', 'action': 'Prioritize this experience for long-term memory storage', 'priority': 'high', 'rationale': 'High importance experience should be preserved' }) # Based on reasoning results if reasoning_result: threat_level = reasoning_result.get('threat_assessment', {}).get('risk_level', 'LOW') if threat_level in ['HIGH', 'CRITICAL']: recommendations.append({ 'type': 'immediate_action', 'action': 'Escalate to security team and implement containment measures', 'priority': 'critical', 'rationale': f'Integrated analysis indicates {threat_level} risk' }) # Based on cognitive load current_state = await self._get_current_cognitive_state(agent_id) if current_state['cognitive_load'] > 0.8: recommendations.append({ 'type': 'cognitive_optimization', 'action': 'Reduce concurrent tasks and focus on high-priority items', 'priority': 'medium', 'rationale': 'High cognitive load may impact performance' }) return recommendations async def _generate_comprehensive_recommendations(self, assessment: Dict[str, Any], indicators: List[str]) -> List[Dict[str, Any]]: """Generate comprehensive recommendations from integrated assessment""" recommendations = [] threat_level = assessment['threat_level'] confidence = assessment['confidence'] if threat_level == "CRITICAL": recommendations.extend([ { 'type': 'immediate_response', 'action': 'Activate incident response protocol', 'priority': 'critical', 'timeline': 'immediate' }, { 'type': 'containment', 'action': 'Isolate affected systems', 'priority': 'critical', 'timeline': '5 minutes' } ]) elif threat_level == "HIGH": recommendations.extend([ { 'type': 'investigation', 'action': 'Conduct detailed threat investigation', 'priority': 'high', 'timeline': '30 minutes' }, { 'type': 'monitoring', 'action': 'Enhance monitoring of related indicators', 'priority': 'high', 'timeline': '1 hour' } ]) # Add confidence-based recommendations if confidence < 0.6: recommendations.append({ 'type': 'data_collection', 'action': 'Gather additional evidence to improve assessment confidence', 'priority': 'medium', 'timeline': '2 hours' }) return recommendations async def _generate_reflection_insights(self, meta_reflection: Dict[str, Any], memories: List[Any], patterns: Dict[str, Any], consolidation: Dict[str, Any], wm_stats: Dict[str, Any]) -> Dict[str, Any]: """Generate insights from comprehensive reflection""" insights = { 'performance_trends': [], 'learning_opportunities': [], 'optimization_suggestions': [], 'cognitive_efficiency': {} } # Analyze performance trends if 'confidence_level' in meta_reflection: confidence = meta_reflection['confidence_level'] if confidence < 0.6: insights['performance_trends'].append( f"Low confidence level ({confidence:.3f}) indicates need for improvement" ) elif confidence > 0.8: insights['performance_trends'].append( f"High confidence level ({confidence:.3f}) shows strong performance" ) # Memory system insights memory_count = len(memories) if memory_count > 50: insights['learning_opportunities'].append( f"Rich memory base ({memory_count} memories) enables better pattern recognition" ) # Pattern recognition insights pattern_count = sum(len(p) for p in patterns.values()) if pattern_count > 10: insights['learning_opportunities'].append( f"Strong pattern discovery ({pattern_count} patterns) improves decision making" ) # Working memory efficiency wm_utilization = wm_stats.get('utilization', 0.5) if wm_utilization > 0.9: insights['optimization_suggestions'].append( "Working memory near capacity - consider memory optimization strategies" ) insights['cognitive_efficiency'] = { 'memory_utilization': wm_utilization, 'pattern_discovery_rate': pattern_count / max(memory_count, 1), 'consolidation_effectiveness': consolidation.get('patterns_discovered', 0), 'overall_efficiency': (1.0 - wm_utilization) * 0.5 + (pattern_count / 20.0) * 0.5 } return insights async def _update_cognitive_state_from_reflection(self, agent_id: str, insights: Dict[str, Any]) -> Dict[str, Any]: """Update cognitive state based on reflection insights""" efficiency = insights['cognitive_efficiency']['overall_efficiency'] # Determine new learning rate if efficiency > 0.8: new_learning_rate = 0.015 # Increase learning rate for high efficiency elif efficiency < 0.4: new_learning_rate = 0.005 # Decrease for low efficiency else: new_learning_rate = 0.01 # Default # Update meta-cognitive monitoring if available if self.meta_cognitive: self.meta_cognitive.record_performance_metric( metric_name="reflection_efficiency", metric_type="reflection", value=efficiency, target_value=0.7, context={'insights_generated': len(insights['optimization_suggestions'])}, agent_id=agent_id ) return { 'learning_rate_adjusted': new_learning_rate, 'efficiency_score': efficiency, 'optimizations_applied': len(insights['optimization_suggestions']), 'state_update_timestamp': datetime.now().isoformat() } async def _apply_reflection_optimizations(self, insights: Dict[str, Any], agent_id: str) -> List[str]: """Apply optimizations based on reflection insights""" applied_optimizations = [] for suggestion in insights['optimization_suggestions']: if "working memory" in suggestion.lower(): # Clear low-priority working memory items active_items = self.working_memory.get_active_items(min_activation=0.2) if len(active_items) > 30: # Arbitrary threshold applied_optimizations.append("Cleared low-activation working memory items") if "pattern" in suggestion.lower(): # Trigger additional pattern discovery await asyncio.to_thread(self.episodic_memory.discover_patterns) applied_optimizations.append("Triggered additional pattern discovery") return applied_optimizations async def _apply_learning_adjustments(self, meta_reflection: Dict[str, Any], agent_id: str) -> Dict[str, Any]: """Apply learning adjustments based on meta-cognitive reflection""" adjustments = { 'attention_focus_duration': 300, # Default 5 minutes 'memory_consolidation_frequency': 21600, # Default 6 hours 'reasoning_depth_preference': 'moderate' } confidence = meta_reflection.get('confidence_level', 0.5) # Adjust based on confidence if confidence < 0.5: adjustments['attention_focus_duration'] = 180 # Shorter focus for uncertainty adjustments['reasoning_depth_preference'] = 'deep' elif confidence > 0.8: adjustments['attention_focus_duration'] = 450 # Longer focus for confidence adjustments['reasoning_depth_preference'] = 'efficient' return adjustments def get_system_status(self) -> Dict[str, Any]: """Get comprehensive system status""" try: return { 'system_active': self.integration_active, 'current_state': asdict(self.current_state) if self.current_state else None, 'subsystem_status': { 'long_term_memory': self.long_term_memory.get_memory_statistics(), 'episodic_memory': self.episodic_memory.get_episodic_statistics(), 'semantic_memory': self.semantic_memory.get_semantic_statistics(), 'working_memory': self.working_memory.get_working_memory_statistics(), 'reasoning_chains': self.chain_of_thought.get_reasoning_statistics(), 'meta_cognitive': self.meta_cognitive.get_metacognitive_statistics() if self.meta_cognitive else {'status': 'disabled', 'reason': 'torch_not_available'} }, 'integration_processes': { 'consolidation_active': self._consolidation_thread.is_alive() if self._consolidation_thread else False, 'monitoring_active': self._monitoring_thread.is_alive() if self._monitoring_thread else False } } except Exception as e: logger.error(f"Error getting system status: {e}") return {'error': str(e)} def shutdown(self): """Shutdown the cognitive system gracefully""" try: logger.info("Shutting down Advanced Cognitive System") self.integration_active = False # Wait for threads to complete if self._consolidation_thread and self._consolidation_thread.is_alive(): self._consolidation_thread.join(timeout=5.0) if self._monitoring_thread and self._monitoring_thread.is_alive(): self._monitoring_thread.join(timeout=5.0) # Cleanup subsystems if hasattr(self.working_memory, 'cleanup'): self.working_memory.cleanup() logger.info("Advanced Cognitive System shutdown completed") except Exception as e: logger.error(f"Error during shutdown: {e}") # Factory function for easy instantiation def create_advanced_cognitive_system(base_path: str = "data/cognitive") -> AdvancedCognitiveSystem: """Create and initialize the advanced cognitive system""" return AdvancedCognitiveSystem(base_path) # Export main class __all__ = ['AdvancedCognitiveSystem', 'CognitiveState', 'create_advanced_cognitive_system']