The Meta-Cognitive Engine is SAFLA's self-awareness and strategic reasoning system. It provides the higher-order cognitive capabilities that enable autonomous learning, goal management, strategy selection, and adaptive behavior. This engine represents the "thinking about thinking" layer that makes SAFLA truly self-aware.
graph TB
subgraph "Meta-Cognitive Engine Architecture"
subgraph "Self-Awareness Layer"
SELF_MODEL[Self-Model]
STATE_MONITOR[State Monitoring]
CAPABILITY_ASSESS[Capability Assessment]
PERFORMANCE_TRACK[Performance Tracking]
end
subgraph "Goal Management Layer"
GOAL_HIERARCHY[Goal Hierarchy]
PRIORITY_MGMT[Priority Management]
GOAL_DECOMP[Goal Decomposition]
PROGRESS_TRACK[Progress Tracking]
end
subgraph "Strategy Layer"
STRATEGY_SELECT[Strategy Selection]
PLAN_GENERATION[Plan Generation]
RESOURCE_ALLOC[Resource Allocation]
ADAPTATION[Strategy Adaptation]
end
subgraph "Learning Layer"
EXPERIENCE_CAPTURE[Experience Capture]
PATTERN_RECOGNITION[Pattern Recognition]
KNOWLEDGE_UPDATE[Knowledge Update]
SKILL_DEVELOPMENT[Skill Development]
end
SELF_MODEL --> GOAL_HIERARCHY
STATE_MONITOR --> PRIORITY_MGMT
CAPABILITY_ASSESS --> GOAL_DECOMP
PERFORMANCE_TRACK --> PROGRESS_TRACK
GOAL_HIERARCHY --> STRATEGY_SELECT
PRIORITY_MGMT --> PLAN_GENERATION
GOAL_DECOMP --> RESOURCE_ALLOC
PROGRESS_TRACK --> ADAPTATION
STRATEGY_SELECT --> EXPERIENCE_CAPTURE
PLAN_GENERATION --> PATTERN_RECOGNITION
RESOURCE_ALLOC --> KNOWLEDGE_UPDATE
ADAPTATION --> SKILL_DEVELOPMENT
EXPERIENCE_CAPTURE --> SELF_MODEL
PATTERN_RECOGNITION --> STATE_MONITOR
KNOWLEDGE_UPDATE --> CAPABILITY_ASSESS
SKILL_DEVELOPMENT --> PERFORMANCE_TRACK
end
style SELF_MODEL fill:#e8f5e8
style GOAL_HIERARCHY fill:#fff3e0
style STRATEGY_SELECT fill:#e1f5fe
style EXPERIENCE_CAPTURE fill:#fce4ec
The self-awareness system maintains a dynamic model of SAFLA's current state, capabilities, and performance characteristics.
graph LR
subgraph "Self-Model Components"
subgraph "Capability Model"
SKILLS[Current Skills]
LIMITATIONS[Known Limitations]
RESOURCES[Available Resources]
PERFORMANCE[Performance Metrics]
end
subgraph "State Model"
CURRENT_STATE[Current State]
CONTEXT[Context Awareness]
ENVIRONMENT[Environment Model]
CONSTRAINTS[Active Constraints]
end
subgraph "Experience Model"
HISTORY[Experience History]
PATTERNS[Learned Patterns]
SUCCESSES[Success Cases]
FAILURES[Failure Cases]
end
subgraph "Prediction Model"
FUTURE_STATE[Future State Prediction]
OUTCOME_PRED[Outcome Prediction]
RISK_PRED[Risk Prediction]
OPPORTUNITY[Opportunity Detection]
end
SKILLS --> CURRENT_STATE
LIMITATIONS --> CONTEXT
RESOURCES --> ENVIRONMENT
PERFORMANCE --> CONSTRAINTS
CURRENT_STATE --> HISTORY
CONTEXT --> PATTERNS
ENVIRONMENT --> SUCCESSES
CONSTRAINTS --> FAILURES
HISTORY --> FUTURE_STATE
PATTERNS --> OUTCOME_PRED
SUCCESSES --> RISK_PRED
FAILURES --> OPPORTUNITY
end
style SKILLS fill:#e8f5e8
style CURRENT_STATE fill:#fff3e0
style HISTORY fill:#e1f5fe
style FUTURE_STATE fill:#fce4ec
from safla.core.meta_cognitive import (
SelfAwarenessEngine,
CapabilityModel,
StateModel,
ExperienceModel,
PredictionModel
)
# Initialize self-awareness engine
self_awareness = SelfAwarenessEngine()
# Define capability model
capability_model = CapabilityModel(
skills={
"memory_operations": {
"proficiency": 0.95,
"confidence": 0.9,
"last_updated": "2024-01-15T10:30:00Z"
},
"pattern_recognition": {
"proficiency": 0.88,
"confidence": 0.85,
"last_updated": "2024-01-15T09:15:00Z"
},
"natural_language_processing": {
"proficiency": 0.92,
"confidence": 0.88,
"last_updated": "2024-01-15T11:00:00Z"
}
},
limitations={
"real_time_processing": {
"severity": "moderate",
"description": "Limited real-time processing for large datasets",
"workarounds": ["batch processing", "streaming optimization"]
},
"external_api_dependency": {
"severity": "low",
"description": "Dependent on external APIs for some operations",
"workarounds": ["local fallbacks", "caching strategies"]
}
},
resources={
"memory": {
"total": 1000000000, # 1GB
"available": 750000000, # 750MB
"utilization": 0.25
},
"cpu": {
"cores": 4,
"utilization": 0.45,
"available_capacity": 0.55
},
"storage": {
"total": 100000000000, # 100GB
"available": 85000000000, # 85GB
"utilization": 0.15
}
}
)
# Initialize state model
state_model = StateModel(
current_state="active_learning",
context={
"current_task": "memory_optimization",
"user_interaction": True,
"system_load": "moderate",
"time_of_day": "morning"
},
environment={
"system_type": "development",
"network_connectivity": "stable",
"external_services": ["available", "responsive"],
"data_sources": ["local_db", "vector_store", "knowledge_base"]
}
)
# Set up self-awareness monitoring
await self_awareness.initialize(
capability_model=capability_model,
state_model=state_model,
update_interval=5.0 # Update every 5 seconds
)
# Get current self-assessment
self_assessment = await self_awareness.get_self_assessment()
print(f"🧠 Self-Assessment:")
print(f" Overall Capability: {self_assessment.overall_capability:.2%}")
print(f" Confidence Level: {self_assessment.confidence_level:.2%}")
print(f" Current State: {self_assessment.current_state}")
print(f" Resource Utilization: {self_assessment.resource_utilization:.2%}")
print(f" Performance Score: {self_assessment.performance_score:.3f}")# Assess specific capabilities
async def assess_memory_capability():
"""Assess current memory operation capabilities."""
# Test memory operations
test_results = []
# Test vector memory
vector_test = await memory_system.test_vector_operations(
test_size=1000,
complexity="medium"
)
test_results.append(("vector_memory", vector_test.performance_score))
# Test episodic memory
episodic_test = await memory_system.test_episodic_operations(
test_size=500,
complexity="medium"
)
test_results.append(("episodic_memory", episodic_test.performance_score))
# Calculate overall capability
overall_score = sum(score for _, score in test_results) / len(test_results)
# Update capability model
await self_awareness.update_capability(
skill_name="memory_operations",
proficiency=overall_score,
confidence=min(overall_score + 0.1, 1.0),
evidence=test_results
)
return overall_score
# Continuous capability monitoring
async def monitor_capabilities():
"""Continuously monitor and update capability assessments."""
while True:
try:
# Assess memory capabilities
memory_score = await assess_memory_capability()
# Assess reasoning capabilities
reasoning_score = await assess_reasoning_capability()
# Assess learning capabilities
learning_score = await assess_learning_capability()
# Update overall self-model
await self_awareness.update_overall_assessment({
"memory": memory_score,
"reasoning": reasoning_score,
"learning": learning_score
})
await asyncio.sleep(300) # Check every 5 minutes
except Exception as e:
logger.error(f"Capability monitoring error: {e}")
await asyncio.sleep(60) # Retry in 1 minute
# Start capability monitoring
asyncio.create_task(monitor_capabilities())The goal management system handles hierarchical goal structures, priority management, and progress tracking.
graph TB
subgraph "Goal Hierarchy Structure"
subgraph "Strategic Goals"
MISSION[Mission Goals]
VISION[Vision Goals]
STRATEGIC[Strategic Objectives]
end
subgraph "Tactical Goals"
OPERATIONAL[Operational Goals]
PROJECT[Project Goals]
MILESTONE[Milestone Goals]
end
subgraph "Immediate Goals"
TASK[Task Goals]
ACTION[Action Goals]
STEP[Step Goals]
end
subgraph "Meta Goals"
LEARNING[Learning Goals]
IMPROVEMENT[Improvement Goals]
ADAPTATION[Adaptation Goals]
end
MISSION --> OPERATIONAL
VISION --> PROJECT
STRATEGIC --> MILESTONE
OPERATIONAL --> TASK
PROJECT --> ACTION
MILESTONE --> STEP
LEARNING --> MISSION
IMPROVEMENT --> VISION
ADAPTATION --> STRATEGIC
end
style MISSION fill:#e8f5e8
style OPERATIONAL fill:#fff3e0
style TASK fill:#e1f5fe
style LEARNING fill:#fce4ec
from safla.core.meta_cognitive import (
GoalManager,
Goal,
GoalType,
Priority,
GoalStatus
)
# Initialize goal manager
goal_manager = GoalManager()
# Define strategic goals
mission_goal = Goal(
id="mission_001",
name="Autonomous Learning Excellence",
description="Achieve autonomous learning capabilities that exceed human-level performance",
goal_type=GoalType.MISSION,
priority=Priority.CRITICAL,
target_completion="2024-12-31",
success_criteria=[
"Learning efficiency > 95%",
"Adaptation time < 1 hour",
"Error rate < 1%"
]
)
# Define operational goals
memory_optimization_goal = Goal(
id="op_001",
name="Memory System Optimization",
description="Optimize memory system for improved performance and efficiency",
goal_type=GoalType.OPERATIONAL,
priority=Priority.HIGH,
parent_goal_id="mission_001",
target_completion="2024-02-15",
success_criteria=[
"Memory access time < 100ms",
"Storage efficiency > 80%",
"Retrieval accuracy > 95%"
]
)
# Define immediate goals
vector_tuning_goal = Goal(
id="task_001",
name="Vector Memory Tuning",
description="Fine-tune vector memory parameters for optimal performance",
goal_type=GoalType.TASK,
priority=Priority.MEDIUM,
parent_goal_id="op_001",
target_completion="2024-01-20",
success_criteria=[
"Vector similarity accuracy > 90%",
"Search time < 50ms",
"Memory usage < 500MB"
]
)
# Add goals to manager
await goal_manager.add_goal(mission_goal)
await goal_manager.add_goal(memory_optimization_goal)
await goal_manager.add_goal(vector_tuning_goal)
# Get goal hierarchy
hierarchy = await goal_manager.get_goal_hierarchy()
print("🎯 Goal Hierarchy:")
for level, goals in hierarchy.items():
print(f" {level.upper()}:")
for goal in goals:
print(f" - {goal.name} ({goal.priority.value})")
print(f" Progress: {goal.progress:.1%}")
print(f" Status: {goal.status.value}")async def decompose_goal(goal_id: str):
"""Automatically decompose a goal into sub-goals."""
goal = await goal_manager.get_goal(goal_id)
if goal.goal_type == GoalType.OPERATIONAL:
# Decompose operational goal into tasks
sub_goals = []
if "memory" in goal.name.lower():
# Memory-related goal decomposition
sub_goals.extend([
Goal(
name="Analyze Current Performance",
description="Analyze current memory system performance metrics",
goal_type=GoalType.TASK,
priority=Priority.HIGH,
parent_goal_id=goal_id
),
Goal(
name="Identify Bottlenecks",
description="Identify performance bottlenecks in memory operations",
goal_type=GoalType.TASK,
priority=Priority.HIGH,
parent_goal_id=goal_id
),
Goal(
name="Implement Optimizations",
description="Implement identified optimization strategies",
goal_type=GoalType.TASK,
priority=Priority.MEDIUM,
parent_goal_id=goal_id
),
Goal(
name="Validate Improvements",
description="Validate that optimizations achieve target metrics",
goal_type=GoalType.TASK,
priority=Priority.HIGH,
parent_goal_id=goal_id
)
])
# Add sub-goals to manager
for sub_goal in sub_goals:
await goal_manager.add_goal(sub_goal)
return sub_goals
return []
# Decompose memory optimization goal
sub_goals = await decompose_goal("op_001")
print(f"📋 Decomposed into {len(sub_goals)} sub-goals")# Update goal progress
async def update_goal_progress(goal_id: str, progress_data: dict):
"""Update goal progress based on performance data."""
goal = await goal_manager.get_goal(goal_id)
# Calculate progress based on success criteria
criteria_met = 0
total_criteria = len(goal.success_criteria)
for criterion in goal.success_criteria:
if evaluate_criterion(criterion, progress_data):
criteria_met += 1
progress = criteria_met / total_criteria
# Update goal
await goal_manager.update_goal_progress(
goal_id=goal_id,
progress=progress,
evidence=progress_data,
timestamp=datetime.utcnow()
)
# Check if goal is completed
if progress >= 1.0:
await goal_manager.complete_goal(goal_id)
# Trigger celebration and learning
await celebrate_goal_completion(goal)
await extract_goal_lessons(goal)
# Automatic progress monitoring
async def monitor_goal_progress():
"""Monitor progress on active goals."""
active_goals = await goal_manager.get_active_goals()
for goal in active_goals:
try:
# Collect relevant performance data
progress_data = await collect_progress_data(goal)
# Update progress
await update_goal_progress(goal.id, progress_data)
# Check for goal conflicts or dependencies
await check_goal_dependencies(goal)
except Exception as e:
logger.error(f"Progress monitoring error for goal {goal.id}: {e}")
# Schedule progress monitoring
asyncio.create_task(monitor_goal_progress())The strategy selection engine chooses optimal approaches based on current context, goals, and available resources.
graph TB
subgraph "Strategy Selection Framework"
subgraph "Strategy Repository"
LEARNED_STRAT[Learned Strategies]
TEMPLATE_STRAT[Template Strategies]
ADAPTIVE_STRAT[Adaptive Strategies]
EMERGENCY_STRAT[Emergency Strategies]
end
subgraph "Selection Criteria"
CONTEXT_MATCH[Context Matching]
RESOURCE_REQ[Resource Requirements]
SUCCESS_PROB[Success Probability]
RISK_ASSESS[Risk Assessment]
end
subgraph "Strategy Evaluation"
COST_BENEFIT[Cost-Benefit Analysis]
TIME_ESTIMATE[Time Estimation]
RESOURCE_IMPACT[Resource Impact]
OUTCOME_PRED[Outcome Prediction]
end
subgraph "Strategy Execution"
PLAN_GEN[Plan Generation]
RESOURCE_ALLOC[Resource Allocation]
MONITORING[Execution Monitoring]
ADAPTATION[Real-time Adaptation]
end
LEARNED_STRAT --> CONTEXT_MATCH
TEMPLATE_STRAT --> RESOURCE_REQ
ADAPTIVE_STRAT --> SUCCESS_PROB
EMERGENCY_STRAT --> RISK_ASSESS
CONTEXT_MATCH --> COST_BENEFIT
RESOURCE_REQ --> TIME_ESTIMATE
SUCCESS_PROB --> RESOURCE_IMPACT
RISK_ASSESS --> OUTCOME_PRED
COST_BENEFIT --> PLAN_GEN
TIME_ESTIMATE --> RESOURCE_ALLOC
RESOURCE_IMPACT --> MONITORING
OUTCOME_PRED --> ADAPTATION
end
style LEARNED_STRAT fill:#e8f5e8
style CONTEXT_MATCH fill:#fff3e0
style COST_BENEFIT fill:#e1f5fe
style PLAN_GEN fill:#fce4ec
from safla.core.meta_cognitive import (
StrategyEngine,
Strategy,
StrategyType,
ExecutionPlan
)
# Initialize strategy engine
strategy_engine = StrategyEngine()
# Define strategy templates
memory_optimization_strategy = Strategy(
name="Incremental Memory Optimization",
strategy_type=StrategyType.OPTIMIZATION,
description="Gradually optimize memory system performance",
context_requirements={
"system_load": "low_to_medium",
"available_memory": "> 500MB",
"user_interaction": "minimal"
},
steps=[
"Analyze current memory usage patterns",
"Identify optimization opportunities",
"Implement low-risk optimizations first",
"Monitor performance improvements",
"Gradually implement higher-risk optimizations",
"Validate overall improvement"
],
expected_duration=timedelta(hours=2),
resource_requirements={
"cpu": 0.3,
"memory": 200000000, # 200MB
"storage": 1000000000 # 1GB
},
success_probability=0.85,
risk_level=0.2
)
# Add strategy to repository
await strategy_engine.add_strategy(memory_optimization_strategy)
# Select strategy for current goal
current_context = {
"goal": "memory_optimization",
"system_load": "medium",
"available_resources": {
"cpu": 0.6,
"memory": 600000000,
"storage": 50000000000
},
"time_constraint": timedelta(hours=4),
"risk_tolerance": 0.3
}
selected_strategy = await strategy_engine.select_strategy(
goal_id="op_001",
context=current_context,
preferences={
"prefer_incremental": True,
"minimize_risk": True,
"maximize_learning": False
}
)
print(f"🧭 Selected Strategy: {selected_strategy.name}")
print(f" Success Probability: {selected_strategy.success_probability:.1%}")
print(f" Risk Level: {selected_strategy.risk_level:.1%}")
print(f" Expected Duration: {selected_strategy.expected_duration}")# Generate execution plan
execution_plan = await strategy_engine.generate_execution_plan(
strategy=selected_strategy,
goal=memory_optimization_goal,
context=current_context
)
print(f"📋 Execution Plan:")
print(f" Total Steps: {len(execution_plan.steps)}")
print(f" Estimated Duration: {execution_plan.estimated_duration}")
print(f" Resource Allocation:")
for resource, allocation in execution_plan.resource_allocation.items():
print(f" {resource}: {allocation}")
print(f" Execution Steps:")
for i, step in enumerate(execution_plan.steps, 1):
print(f" {i}. {step.name}")
print(f" Duration: {step.estimated_duration}")
print(f" Dependencies: {step.dependencies}")
print(f" Success Criteria: {step.success_criteria}")
# Execute plan with monitoring
async def execute_plan_with_monitoring(plan: ExecutionPlan):
"""Execute plan with real-time monitoring and adaptation."""
execution_context = {
"start_time": datetime.utcnow(),
"current_step": 0,
"completed_steps": [],
"performance_metrics": {}
}
for step in plan.steps:
try:
print(f"🔄 Executing step: {step.name}")
# Check dependencies
if not await check_step_dependencies(step, execution_context):
print(f"⚠️ Dependencies not met for step: {step.name}")
continue
# Execute step
step_result = await execute_step(step, execution_context)
# Evaluate success
if step_result.success:
execution_context["completed_steps"].append(step)
print(f"✅ Step completed: {step.name}")
else:
print(f"❌ Step failed: {step.name}")
# Attempt recovery or adaptation
recovery_action = await strategy_engine.get_recovery_action(
step=step,
failure_reason=step_result.error,
context=execution_context
)
if recovery_action:
print(f"🔧 Attempting recovery: {recovery_action.description}")
await execute_recovery_action(recovery_action)
else:
print(f"🚨 No recovery action available")
break
# Update performance metrics
execution_context["performance_metrics"].update(step_result.metrics)
except Exception as e:
logger.error(f"Step execution error: {e}")
break
return execution_context
# Start plan execution
execution_result = await execute_plan_with_monitoring(execution_plan)The adaptive learning system enables SAFLA to learn from experience and continuously improve its capabilities.
graph TB
subgraph "Adaptive Learning System"
subgraph "Experience Collection"
ACTION_LOG[Action Logging]
OUTCOME_TRACK[Outcome Tracking]
CONTEXT_CAPTURE[Context Capture]
FEEDBACK_COLLECT[Feedback Collection]
end
subgraph "Pattern Recognition"
SUCCESS_PATTERNS[Success Patterns]
FAILURE_PATTERNS[Failure Patterns]
CONTEXT_PATTERNS[Context Patterns]
STRATEGY_PATTERNS[Strategy Patterns]
end
subgraph "Knowledge Extraction"
RULE_EXTRACTION[Rule Extraction]
HEURISTIC_GEN[Heuristic Generation]
STRATEGY_REFINEMENT[Strategy Refinement]
MODEL_UPDATE[Model Update]
end
subgraph "Application"
STRATEGY_IMPROVE[Strategy Improvement]
CAPABILITY_ENHANCE[Capability Enhancement]
PREDICTION_REFINE[Prediction Refinement]
ADAPTATION_SPEED[Adaptation Speed]
end
ACTION_LOG --> SUCCESS_PATTERNS
OUTCOME_TRACK --> FAILURE_PATTERNS
CONTEXT_CAPTURE --> CONTEXT_PATTERNS
FEEDBACK_COLLECT --> STRATEGY_PATTERNS
SUCCESS_PATTERNS --> RULE_EXTRACTION
FAILURE_PATTERNS --> HEURISTIC_GEN
CONTEXT_PATTERNS --> STRATEGY_REFINEMENT
STRATEGY_PATTERNS --> MODEL_UPDATE
RULE_EXTRACTION --> STRATEGY_IMPROVE
HEURISTIC_GEN --> CAPABILITY_ENHANCE
STRATEGY_REFINEMENT --> PREDICTION_REFINE
MODEL_UPDATE --> ADAPTATION_SPEED
end
style ACTION_LOG fill:#e8f5e8
style SUCCESS_PATTERNS fill:#fff3e0
style RULE_EXTRACTION fill:#e1f5fe
style STRATEGY_IMPROVE fill:#fce4ec
from safla.core.meta_cognitive import (
AdaptiveLearningEngine,
Experience,
LearningPattern,
KnowledgeUpdate
)
# Initialize adaptive learning engine
learning_engine = AdaptiveLearningEngine()
# Capture experience
async def capture_experience(action, context, outcome):
"""Capture an experience for learning."""
experience = Experience(
timestamp=datetime.utcnow(),
action=action,
context=context,
outcome=outcome,
success=outcome.get("success", False),
performance_metrics=outcome.get("metrics", {}),
feedback=outcome.get("feedback", {})
)
await learning_engine.add_experience(experience)
# Trigger pattern recognition if enough experiences
experience_count = await learning_engine.get_experience_count()
if experience_count % 100 == 0: # Every 100 experiences
await learning_engine.analyze_patterns()
# Pattern recognition
async def analyze_success_patterns():
"""Analyze patterns in successful experiences."""
successful_experiences = await learning_engine.get_experiences(
filter_criteria={"success": True},
limit=1000
)
patterns = []
# Analyze context patterns
context_clusters = await learning_engine.cluster_contexts(successful_experiences)
for cluster in context_clusters:
pattern = LearningPattern(
type="context_success",
description=f"Success pattern in {cluster.context_type}",
conditions=cluster.common_conditions,
confidence=cluster.confidence,
evidence_count=len(cluster.experiences)
)
patterns.append(pattern)
# Analyze action patterns
action_sequences = await learning_engine.extract_action_sequences(successful_experiences)
for sequence in action_sequences:
pattern = LearningPattern(
type="action_sequence",
description=f"Successful action sequence: {sequence.name}",
conditions=sequence.preconditions,
actions=sequence.actions,
confidence=sequence.success_rate,
evidence_count=sequence.occurrence_count
)
patterns.append(pattern)
return patterns
# Knowledge update
async def update_knowledge_base(patterns):
"""Update knowledge base with learned patterns."""
for pattern in patterns:
if pattern.confidence > 0.8: # High confidence patterns
# Update strategy repository
if pattern.type == "action_sequence":
new_strategy = await learning_engine.create_strategy_from_pattern(pattern)
await strategy_engine.add_strategy(new_strategy)
# Update capability model
elif pattern.type == "context_success":
capability_update = await learning_engine.extract_capability_insight(pattern)
await self_awareness.update_capability_model(capability_update)
# Update prediction models
prediction_update = await learning_engine.create_prediction_rule(pattern)
await self_awareness.update_prediction_model(prediction_update)
# Continuous learning loop
async def continuous_learning():
"""Continuous learning and adaptation loop."""
while True:
try:
# Analyze recent patterns
patterns = await analyze_success_patterns()
failure_patterns = await analyze_failure_patterns()
# Update knowledge base
await update_knowledge_base(patterns + failure_patterns)
# Refine existing strategies
await refine_strategies_based_on_learning()
# Update self-model
await update_self_model_from_learning()
await asyncio.sleep(3600) # Learn every hour
except Exception as e:
logger.error(f"Continuous learning error: {e}")
await asyncio.sleep(300) # Retry in 5 minutes
# Start continuous learning
asyncio.create_task(continuous_learning())# Meta-learning: Learning how to learn better
async def meta_learning_analysis():
"""Analyze and improve the learning process itself."""
# Analyze learning effectiveness
learning_metrics = await learning_engine.get_learning_metrics()
print(f"📊 Learning Effectiveness Analysis:")
print(f" Pattern Recognition Accuracy: {learning_metrics.pattern_accuracy:.2%}")
print(f" Knowledge Application Success: {learning_metrics.application_success:.2%}")
print(f" Learning Speed: {learning_metrics.learning_speed:.2f} patterns/hour")
print(f" Adaptation Time: {learning_metrics.adaptation_time:.1f} minutes")
# Identify learning bottlenecks
bottlenecks = await learning_engine.identify_learning_bottlenecks()
if bottlenecks:
print(f"⚠️ Learning Bottlenecks:")
for bottleneck in bottlenecks:
print(f" - {bottleneck.component}: {bottleneck.description}")
print(f" Impact: {bottleneck.impact:.1%}")
print(f" Suggested Fix: {bottleneck.suggested_fix}")
# Optimize learning parameters
optimization_suggestions = await learning_engine.optimize_learning_parameters()
for suggestion in optimization_suggestions:
print(f"🔧 Learning Optimization: {suggestion.parameter}")
print(f" Current Value: {suggestion.current_value}")
print(f" Suggested Value: {suggestion.suggested_value}")
print(f" Expected Improvement: {suggestion.expected_improvement:.1%}")
# Apply optimization if beneficial
if suggestion.expected_improvement > 0.1: # 10% improvement threshold
await learning_engine.update_parameter(
suggestion.parameter,
suggestion.suggested_value
)
# Schedule meta-learning analysis
asyncio.create_task(meta_learning_analysis())The meta-cognitive engine integrates seamlessly with other SAFLA components.
# Integration with memory system
async def integrate_with_memory():
"""Integrate meta-cognitive insights with memory system."""
# Use self-awareness to optimize memory operations
memory_performance = await self_awareness.assess_component_performance("memory_system")
if memory_performance.efficiency < 0.8:
# Generate memory optimization strategy
optimization_strategy = await strategy_engine.generate_optimization_strategy(
component="memory_system",
current_performance=memory_performance,
target_performance=0.9
)
# Execute optimization
await execute_strategy(optimization_strategy)
# Integration with safety system
async def integrate_with_safety():
"""Integrate meta-cognitive insights with safety system."""
# Use goal management to inform safety constraints
active_goals = await goal_manager.get_active_goals()
critical_goals = [g for g in active_goals if g.priority == Priority.CRITICAL]
# Adjust safety constraints based on critical goals
for goal in critical_goals:
safety_adjustments = await strategy_engine.generate_safety_adjustments(goal)
await safety_framework.apply_adjustments(safety_adjustments)
# Integration with learning system
async def integrate_with_learning():
"""Integrate meta-cognitive insights with learning system."""
# Use strategy success patterns to improve learning
strategy_patterns = await learning_engine.get_strategy_patterns()
for pattern in strategy_patterns:
if pattern.success_rate > 0.9:
# Promote successful strategy patterns
await strategy_engine.promote_strategy_pattern(pattern)
elif pattern.success_rate < 0.3:
# Deprecate unsuccessful strategy patterns
await strategy_engine.deprecate_strategy_pattern(pattern)
# Orchestrate system integration
async def orchestrate_system_integration():
"""Orchestrate integration across all systems."""
integration_tasks = [
integrate_with_memory(),
integrate_with_safety(),
integrate_with_learning()
]
await asyncio.gather(*integration_tasks)
# Schedule regular integration
asyncio.create_task(orchestrate_system_integration())Next: Safety & Validation - Comprehensive safety framework
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