""" Meta-Cognitive Capabilities for Cyber-LLM Self-reflection, adaptation, and cognitive load management Author: Muzan Sano """ import asyncio import json import logging import numpy as np from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum import torch import torch.nn as nn from collections import deque from ..utils.logging_system import CyberLLMLogger, CyberLLMError, ErrorCategory from ..memory.persistent_memory import PersistentMemoryManager from ..memory.strategic_planning import StrategicPlanningEngine class CognitiveState(Enum): """Cognitive processing states""" OPTIMAL = "optimal" MODERATE_LOAD = "moderate_load" HIGH_LOAD = "high_load" OVERLOADED = "overloaded" RECOVERING = "recovering" class AdaptationStrategy(Enum): """Learning adaptation strategies""" AGGRESSIVE = "aggressive" MODERATE = "moderate" CONSERVATIVE = "conservative" CAUTIOUS = "cautious" @dataclass class CognitiveMetrics: """Cognitive performance metrics""" timestamp: datetime # Performance metrics task_completion_rate: float accuracy_score: float response_time: float resource_utilization: float # Cognitive load indicators attention_fragmentation: float # 0-1, higher = more fragmented working_memory_usage: float # 0-1, percentage used processing_complexity: float # 0-1, task complexity measure # Adaptation metrics learning_rate: float confidence_level: float adaptation_success_rate: float # Error metrics error_count: int critical_errors: int recovery_time: Optional[float] = None @dataclass class SelfReflectionResult: """Results from self-reflection analysis""" reflection_id: str timestamp: datetime # Performance assessment strengths: List[str] weaknesses: List[str] improvement_areas: List[str] # Strategy effectiveness effective_strategies: List[str] ineffective_strategies: List[str] recommended_changes: List[str] # Cognitive insights cognitive_patterns: Dict[str, Any] load_management_insights: List[str] attention_allocation_insights: List[str] # Action items immediate_adjustments: List[str] medium_term_goals: List[str] long_term_objectives: List[str] class MetaCognitiveEngine: """Advanced meta-cognitive capabilities for self-reflection and adaptation""" def __init__(self, memory_manager: PersistentMemoryManager, strategic_planner: StrategicPlanningEngine, logger: Optional[CyberLLMLogger] = None): self.memory_manager = memory_manager self.strategic_planner = strategic_planner self.logger = logger or CyberLLMLogger(name="meta_cognitive") # Cognitive state tracking self.current_state = CognitiveState.OPTIMAL self.state_history = deque(maxlen=1000) self.cognitive_metrics = deque(maxlen=10000) # Self-reflection system self.reflection_history = {} self.performance_baselines = {} self.adaptation_strategies = {} # Cognitive load management self.attention_allocator = AttentionAllocator() self.cognitive_load_monitor = CognitiveLoadMonitor() # Learning optimization self.learning_rate_optimizer = LearningRateOptimizer() self.strategy_evaluator = StrategyEvaluator() # Neural networks for meta-learning self.performance_predictor = self._build_performance_predictor() self.strategy_selector = self._build_strategy_selector() self.logger.info("Meta-Cognitive Engine initialized") async def conduct_self_reflection(self, time_period: timedelta = timedelta(hours=1)) -> SelfReflectionResult: """Conduct comprehensive self-reflection analysis""" reflection_id = f"reflection_{datetime.now().strftime('%Y%m%d_%H%M%S')}" try: self.logger.info("Starting self-reflection analysis", reflection_id=reflection_id) # Gather performance data end_time = datetime.now() start_time = end_time - time_period performance_data = await self._gather_performance_data(start_time, end_time) cognitive_data = await self._gather_cognitive_data(start_time, end_time) strategy_data = await self._gather_strategy_data(start_time, end_time) # Analyze strengths and weaknesses strengths, weaknesses = await self._analyze_performance_patterns(performance_data) # Evaluate strategy effectiveness effective_strategies, ineffective_strategies = await self._evaluate_strategies(strategy_data) # Generate insights cognitive_patterns = await self._analyze_cognitive_patterns(cognitive_data) load_insights = await self._analyze_load_management(cognitive_data) attention_insights = await self._analyze_attention_allocation(cognitive_data) # Generate recommendations immediate_adjustments = await self._generate_immediate_adjustments( weaknesses, ineffective_strategies, cognitive_patterns) medium_term_goals = await self._generate_medium_term_goals( strengths, weaknesses, cognitive_patterns) long_term_objectives = await self._generate_long_term_objectives( performance_data, cognitive_patterns) # Create reflection result reflection_result = SelfReflectionResult( reflection_id=reflection_id, timestamp=datetime.now(), strengths=strengths, weaknesses=weaknesses, improvement_areas=list(set(weaknesses + [adj.split(':')[0] for adj in immediate_adjustments])), effective_strategies=effective_strategies, ineffective_strategies=ineffective_strategies, recommended_changes=immediate_adjustments + medium_term_goals, cognitive_patterns=cognitive_patterns, load_management_insights=load_insights, attention_allocation_insights=attention_insights, immediate_adjustments=immediate_adjustments, medium_term_goals=medium_term_goals, long_term_objectives=long_term_objectives ) # Store reflection result self.reflection_history[reflection_id] = reflection_result # Store in persistent memory await self.memory_manager.store_reasoning_chain( chain_id=f"self_reflection_{reflection_id}", steps=[ f"Analyzed performance over {time_period}", f"Identified {len(strengths)} strengths and {len(weaknesses)} weaknesses", f"Evaluated {len(effective_strategies)} effective strategies", f"Generated {len(immediate_adjustments)} immediate adjustments" ], conclusion=f"Self-reflection completed with actionable insights", confidence=0.85, metadata={ "reflection_type": "comprehensive_analysis", "time_period": str(time_period), "performance_score": np.mean([m.accuracy_score for m in self.cognitive_metrics if m.timestamp >= start_time]) } ) self.logger.info("Self-reflection analysis completed", reflection_id=reflection_id, strengths_count=len(strengths), weaknesses_count=len(weaknesses), recommendations_count=len(immediate_adjustments)) return reflection_result except Exception as e: self.logger.error("Self-reflection analysis failed", error=str(e)) raise CyberLLMError("Self-reflection failed", ErrorCategory.COGNITIVE_ERROR) async def optimize_learning_rate(self, recent_performance: List[float], task_complexity: float) -> float: """Optimize learning rate based on recent performance and task complexity""" try: # Analyze performance trends performance_trend = self._calculate_performance_trend(recent_performance) performance_variance = np.var(recent_performance) # Current learning rate current_lr = self.learning_rate_optimizer.get_current_rate() # Adaptation strategy based on performance if performance_trend > 0.1 and performance_variance < 0.05: # Good performance, stable -> slightly increase learning rate adaptation_factor = 1.1 strategy = AdaptationStrategy.AGGRESSIVE elif performance_trend > 0.05: # Moderate improvement -> maintain or slight increase adaptation_factor = 1.05 strategy = AdaptationStrategy.MODERATE elif performance_trend < -0.1 or performance_variance > 0.2: # Poor performance or high variance -> decrease learning rate adaptation_factor = 0.8 strategy = AdaptationStrategy.CAUTIOUS else: # Stable performance -> minor adjustment based on complexity adaptation_factor = 1.0 - (task_complexity - 0.5) * 0.1 strategy = AdaptationStrategy.CONSERVATIVE # Apply complexity adjustment complexity_factor = 1.0 - (task_complexity * 0.3) final_factor = adaptation_factor * complexity_factor # Calculate new learning rate new_lr = current_lr * final_factor new_lr = np.clip(new_lr, 0.0001, 0.1) # Keep within reasonable bounds # Update learning rate optimizer self.learning_rate_optimizer.update_rate(new_lr, strategy) self.logger.info("Learning rate optimized", old_rate=current_lr, new_rate=new_lr, strategy=strategy.value, performance_trend=performance_trend) return new_lr except Exception as e: self.logger.error("Learning rate optimization failed", error=str(e)) return self.learning_rate_optimizer.get_current_rate() async def manage_cognitive_load(self, current_tasks: List[Dict[str, Any]], available_resources: Dict[str, float]) -> Dict[str, Any]: """Manage cognitive load and optimize task allocation""" try: # Calculate current cognitive load current_load = await self._calculate_cognitive_load(current_tasks) # Determine cognitive state new_state = self._determine_cognitive_state(current_load, available_resources) # Update state if changed if new_state != self.current_state: self.logger.info("Cognitive state changed", old_state=self.current_state.value, new_state=new_state.value) self.current_state = new_state self.state_history.append((datetime.now(), new_state)) # Generate load management strategy management_strategy = await self._generate_load_management_strategy( current_load, new_state, current_tasks, available_resources) # Apply attention allocation optimization attention_allocation = await self.attention_allocator.optimize_allocation( current_tasks, available_resources, new_state) # Generate recommendations recommendations = await self._generate_load_management_recommendations( current_load, new_state, management_strategy) result = { "cognitive_state": new_state.value, "cognitive_load": current_load, "management_strategy": management_strategy, "attention_allocation": attention_allocation, "recommendations": recommendations, "resource_adjustments": await self._calculate_resource_adjustments( new_state, available_resources) } self.logger.info("Cognitive load management completed", state=new_state.value, load=current_load, recommendations_count=len(recommendations)) return result except Exception as e: self.logger.error("Cognitive load management failed", error=str(e)) return {"error": str(e)} def _build_performance_predictor(self) -> nn.Module: """Build neural network for performance prediction""" class PerformancePredictor(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(20, 64) # Input: various metrics self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 16) self.fc4 = nn.Linear(16, 1) # Output: predicted performance self.dropout = nn.Dropout(0.2) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.dropout(x) x = torch.relu(self.fc2(x)) x = self.dropout(x) x = torch.relu(self.fc3(x)) x = torch.sigmoid(self.fc4(x)) return x return PerformancePredictor() def _build_strategy_selector(self) -> nn.Module: """Build neural network for strategy selection""" class StrategySelector(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(15, 48) # Input: context features self.fc2 = nn.Linear(48, 24) self.fc3 = nn.Linear(24, 8) # Output: strategy probabilities self.dropout = nn.Dropout(0.15) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.dropout(x) x = torch.relu(self.fc2(x)) x = torch.softmax(self.fc3(x), dim=-1) return x return StrategySelector() class AttentionAllocator: """Manages dynamic attention allocation across tasks""" def __init__(self): self.attention_weights = {} self.priority_scores = {} self.allocation_history = deque(maxlen=1000) async def optimize_allocation(self, tasks: List[Dict[str, Any]], resources: Dict[str, float], cognitive_state: CognitiveState) -> Dict[str, float]: """Optimize attention allocation across tasks""" # Calculate base priority scores for task in tasks: task_id = task.get('id', str(hash(str(task)))) priority = task.get('priority', 0.5) complexity = task.get('complexity', 0.5) deadline_pressure = task.get('deadline_pressure', 0.0) # Adjust priority based on cognitive state state_multiplier = { CognitiveState.OPTIMAL: 1.0, CognitiveState.MODERATE_LOAD: 0.9, CognitiveState.HIGH_LOAD: 0.7, CognitiveState.OVERLOADED: 0.5, CognitiveState.RECOVERING: 0.6 }.get(cognitive_state, 1.0) adjusted_priority = (priority * 0.4 + deadline_pressure * 0.4 + (1.0 - complexity) * 0.2) * state_multiplier self.priority_scores[task_id] = adjusted_priority # Normalize allocation total_priority = sum(self.priority_scores.values()) if total_priority > 0: allocation = {task_id: score / total_priority for task_id, score in self.priority_scores.items()} else: # Equal allocation if no priorities equal_weight = 1.0 / len(tasks) if tasks else 0.0 allocation = {task.get('id', str(i)): equal_weight for i, task in enumerate(tasks)} # Store allocation history self.allocation_history.append((datetime.now(), allocation)) return allocation class CognitiveLoadMonitor: """Monitors and analyzes cognitive load patterns""" def __init__(self): self.load_history = deque(maxlen=10000) self.load_patterns = {} def calculate_load(self, active_tasks: int, task_complexity: float, resource_usage: float, error_rate: float) -> float: """Calculate current cognitive load""" # Base load from task count (logarithmic scaling) task_load = min(np.log(active_tasks + 1) / np.log(10), 1.0) # Complexity contribution complexity_load = task_complexity * 0.3 # Resource pressure resource_load = resource_usage * 0.25 # Error pressure (exponential) error_load = min(error_rate ** 0.5, 1.0) * 0.2 total_load = task_load + complexity_load + resource_load + error_load # Store in history self.load_history.append((datetime.now(), total_load)) return min(total_load, 1.0) class LearningRateOptimizer: """Optimizes learning rates based on performance feedback""" def __init__(self, initial_rate: float = 0.001): self.current_rate = initial_rate self.rate_history = deque(maxlen=1000) self.performance_history = deque(maxlen=1000) self.strategy_effectiveness = {} def get_current_rate(self) -> float: return self.current_rate def update_rate(self, new_rate: float, strategy: AdaptationStrategy): self.rate_history.append((datetime.now(), self.current_rate, new_rate, strategy)) self.current_rate = new_rate class StrategyEvaluator: """Evaluates effectiveness of different strategies""" def __init__(self): self.strategy_outcomes = {} self.strategy_scores = {} def record_strategy_outcome(self, strategy: str, outcome_score: float): if strategy not in self.strategy_outcomes: self.strategy_outcomes[strategy] = deque(maxlen=100) self.strategy_outcomes[strategy].append((datetime.now(), outcome_score)) # Update average score scores = [score for _, score in self.strategy_outcomes[strategy]] self.strategy_scores[strategy] = np.mean(scores) # Factory function def create_meta_cognitive_engine(memory_manager: PersistentMemoryManager, strategic_planner: StrategicPlanningEngine, **kwargs) -> MetaCognitiveEngine: """Create meta-cognitive engine""" return MetaCognitiveEngine(memory_manager, strategic_planner, **kwargs)