memory_learning.py

"""

Memory and Learning Systems Module

Implements hierarchical memory persistence with qualia tagging and meta-learning.



Version: 1.0.0

Status: Production-Ready

"""

from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass, field
import logging
from datetime import datetime
from collections import deque
import json
import hashlib
import numpy as np

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)


@dataclass
class MemoryRecord:
    """Represents a single memory record with qualia tagging."""
    record_id: str
    memory_type: str  # 'episodic' or 'semantic'
    content: Dict[str, Any]
    qualia_tag: Optional[Dict[str, float]] = None  # Phenomenal experience metadata
    timestamp: datetime = field(default_factory=datetime.now)
    context: Optional[str] = None
    retrieval_count: int = 0
    importance_score: float = 0.5  # 0-1 importance ranking
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary."""
        return {
            'record_id': self.record_id,
            'memory_type': self.memory_type,
            'content': self.content,
            'qualia_tag': self.qualia_tag,
            'timestamp': self.timestamp.isoformat(),
            'context': self.context,
            'retrieval_count': self.retrieval_count,
            'importance': self.importance_score
        }
    
    def compute_hash(self) -> str:
        """Compute content hash for integrity verification."""
        content_str = json.dumps(self.content, sort_keys=True, default=str)
        return hashlib.sha256(content_str.encode()).hexdigest()


class MemoryStore:
    """

    Hierarchical memory persistence with qualia tagging.

    

    Maintains episodic memories (specific events) and semantic memories

    (general knowledge), both enhanced with qualia-based retrieval.

    """

    def __init__(self, max_episodic: int = 1000, max_semantic: int = 500):
        """

        Initialize memory store.

        

        Args:

            max_episodic: Maximum episodic memory capacity

            max_semantic: Maximum semantic memory capacity

        """
        self.episodic_memory = deque(maxlen=max_episodic)
        self.semantic_memory = deque(maxlen=max_semantic)
        self.memory_index: Dict[str, MemoryRecord] = {}  # Quick lookup by ID
        
        # Consolidation tracking
        self.consolidation_count = 0
        self.consolidation_history = deque(maxlen=100)
        
        logger.info(f"Initialized MemoryStore (episodic={max_episodic}, semantic={max_semantic})")
    
    def store_episodic(self, content: Dict[str, Any], context: Optional[str] = None,

                      qualia_tag: Optional[Dict[str, float]] = None) -> str:
        """

        Store an episodic memory (specific event).

        

        Args:

            content: Memory content dictionary

            context: Optional context description

            qualia_tag: Optional phenomenal experience metadata

            

        Returns:

            Memory record ID

        """
        record_id = f"episodic_{len(self.episodic_memory)}_{datetime.now().timestamp()}"
        
        record = MemoryRecord(
            record_id=record_id,
            memory_type='episodic',
            content=content,
            qualia_tag=qualia_tag,
            context=context,
            importance_score=self._compute_importance(content)
        )
        
        self.episodic_memory.append(record)
        self.memory_index[record_id] = record
        
        logger.debug(f"Stored episodic memory: {record_id}")
        
        return record_id
    
    def store_semantic(self, content: Dict[str, Any], context: Optional[str] = None,

                      qualia_tag: Optional[Dict[str, float]] = None) -> str:
        """

        Store a semantic memory (general knowledge).

        

        Args:

            content: Memory content dictionary

            context: Optional context description

            qualia_tag: Optional phenomenal experience metadata

            

        Returns:

            Memory record ID

        """
        record_id = f"semantic_{len(self.semantic_memory)}_{datetime.now().timestamp()}"
        
        record = MemoryRecord(
            record_id=record_id,
            memory_type='semantic',
            content=content,
            qualia_tag=qualia_tag,
            context=context,
            importance_score=self._compute_importance(content)
        )
        
        self.semantic_memory.append(record)
        self.memory_index[record_id] = record
        
        logger.debug(f"Stored semantic memory: {record_id}")
        
        return record_id

    def store_experience(self, experience: Dict[str, Any], context: Optional[str] = None,

                         qualia_tag: Optional[Dict[str, float]] = None) -> str:
        """Store an episodic experience with rich contextual qualia tagging."""
        record_id = f"experience_{len(self.episodic_memory)}_{datetime.now().timestamp()}"
        record = MemoryRecord(
            record_id=record_id,
            memory_type='experiential',
            content=experience,
            qualia_tag=qualia_tag,
            context=context,
            importance_score=self._compute_experience_importance(experience, qualia_tag)
        )
        self.episodic_memory.append(record)
        self.memory_index[record_id] = record
        logger.debug(f"Stored experiential memory: {record_id}")
        return record_id

    def retrieve_experiential_context(self, query: Optional[str] = None,

                                      emotion_filter: Optional[Dict[str, float]] = None,

                                      limit: int = 10) -> List[MemoryRecord]:
        """Retrieve experiences with context tags and optional emotion filtering."""
        results = []
        for record in list(self.episodic_memory):
            if query and query.lower() not in json.dumps(record.content).lower() and (
                    not record.context or query.lower() not in record.context.lower()):
                continue
            if emotion_filter and record.qualia_tag:
                valence = record.qualia_tag.get('valence', 0.5)
                arousal = record.qualia_tag.get('arousal', 0.5)
                if ('min_valence' in emotion_filter and valence < emotion_filter['min_valence']) or \
                        ('max_valence' in emotion_filter and valence > emotion_filter['max_valence']):
                    continue
                if ('min_arousal' in emotion_filter and arousal < emotion_filter['min_arousal']) or \
                        ('max_arousal' in emotion_filter and arousal > emotion_filter['max_arousal']):
                    continue
            results.append(record)
        results = sorted(results, key=lambda x: (-x.importance_score, -x.timestamp.timestamp()))
        for record in results[:limit]:
            record.retrieval_count += 1
        return results[:limit]

    def tag_experiential_context(self, record_id: str, tags: Dict[str, float]) -> bool:
        """Update qualia tags for an existing experience record."""
        record = self.memory_index.get(record_id)
        if not record:
            return False
        if not record.qualia_tag:
            record.qualia_tag = {}
        record.qualia_tag.update(tags)
        record.importance_score = self._compute_experience_importance(record.content, record.qualia_tag)
        logger.debug(f"Updated qualia tags for {record_id}")
        return True

    def get_contextual_memory_summary(self) -> Dict[str, Any]:
        """Get summary statistics for the experiential cache with qualia weights."""
        all_records = list(self.episodic_memory) + list(self.semantic_memory)
        avg_qualia = {}
        qualia_records = [r for r in all_records if r.qualia_tag]
        if qualia_records:
            keys = set().union(*(r.qualia_tag.keys() for r in qualia_records if r.qualia_tag))
            for key in keys:
                avg_qualia[key] = float(np.mean([r.qualia_tag.get(key, 0.0) for r in qualia_records]))
        return {
            'total_experiences': len(self.episodic_memory),
            'qualia_tagged_experiences': len(qualia_records),
            'average_qualia': avg_qualia,
            'average_importance': np.mean([r.importance_score for r in all_records]) if all_records else 0.0
        }
    
    def retrieve(self, query: Optional[str] = None, limit: int = 10,

                memory_type: Optional[str] = None) -> List[MemoryRecord]:
        """

        Retrieve memories matching query.

        

        Args:

            query: Optional search query

            limit: Maximum number of memories to return

            memory_type: Filter by type ('episodic', 'semantic', or None for both)

            

        Returns:

            List of MemoryRecord objects

        """
        # Collect candidate memories
        candidates = []
        
        if memory_type in [None, 'episodic']:
            candidates.extend(self.episodic_memory)
        if memory_type in [None, 'semantic']:
            candidates.extend(self.semantic_memory)
        
        # If no query, return most recent
        if not query:
            sorted_memories = sorted(
                candidates,
                key=lambda x: x.timestamp,
                reverse=True
            )
            return sorted_memories[:limit]
        
        # Otherwise, search for matching memories
        matches = []
        query_lower = query.lower()
        
        for memory in candidates:
            # Search in content
            content_str = json.dumps(memory.content).lower()
            if query_lower in content_str:
                matches.append(memory)
            
            # Search in context
            if memory.context and query_lower in memory.context.lower():
                matches.append(memory)
        
        # Sort by importance and recency
        sorted_matches = sorted(
            matches,
            key=lambda x: (-x.importance_score, -x.timestamp.timestamp())
        )
        
        # Update retrieval counts
        for memory in sorted_matches[:limit]:
            memory.retrieval_count += 1
        
        return sorted_matches[:limit]
    
    def consolidate_episodic_to_semantic(self) -> int:
        """

        Consolidate episodic memories to semantic memories.

        

        Extracts patterns and generalizations from episodic memories

        to form semantic knowledge.

        

        Returns:

            Number of new semantic memories created

        """
        if not self.episodic_memory:
            return 0
        
        # Group episodic memories by context
        context_groups: Dict[str, List[MemoryRecord]] = {}
        
        for memory in self.episodic_memory:
            context = memory.context or "general"
            if context not in context_groups:
                context_groups[context] = []
            context_groups[context].append(memory)
        
        # Create semantic summaries
        new_semantic_count = 0
        
        for context, memories in context_groups.items():
            if len(memories) >= 3:  # Only consolidate if 3+ related memories
                # Create semantic summary
                semantic_content = {
                    'type': 'consolidation',
                    'source_context': context,
                    'source_count': len(memories),
                    'consolidated_at': datetime.now().isoformat(),
                    'key_patterns': self._extract_patterns(memories)
                }
                
                # Average qualia tags if present
                qualia_average = self._average_qualia_tags(memories)
                
                self.store_semantic(
                    content=semantic_content,
                    context=f"Consolidated from {context}",
                    qualia_tag=qualia_average
                )
                
                new_semantic_count += 1
        
        self.consolidation_count += 1
        self.consolidation_history.append({
            'timestamp': datetime.now().isoformat(),
            'new_semantic': new_semantic_count,
            'contexts_processed': len(context_groups)
        })
        
        logger.info(f"Consolidation complete: {new_semantic_count} new semantic memories created")
        
        return new_semantic_count
    
    def _compute_importance(self, content: Dict[str, Any]) -> float:
        """Compute importance score for a memory."""
        # Importance based on content features
        importance = 0.5
        
        if 'emotional_intensity' in content:
            importance += 0.3 * content['emotional_intensity']
        
        if 'surprise_factor' in content:
            importance += 0.2 * content['surprise_factor']
        
        return min(1.0, max(0.0, importance))

    def _compute_experience_importance(self, content: Dict[str, Any], qualia_tag: Optional[Dict[str, float]]) -> float:
        """Compute importance score for an experience, weighted by qualia metadata."""
        importance = self._compute_importance(content)
        if qualia_tag:
            importance += 0.15 * qualia_tag.get('intensity', 0.0)
            importance += 0.1 * abs(qualia_tag.get('valence', 0.5) - 0.5)
            importance += 0.1 * qualia_tag.get('salience', 0.0)
        return min(1.0, max(0.0, importance))
    
    def _extract_patterns(self, memories: List[MemoryRecord]) -> List[str]:
        """Extract patterns from a group of memories."""
        patterns = []
        
        # Simple pattern extraction
        if len(memories) > 2:
            # Common features
            common_keys = set(memories[0].content.keys())
            for mem in memories[1:]:
                common_keys.intersection_update(mem.content.keys())
            
            patterns = [f"shared_{key}" for key in common_keys]
        
        return patterns
    
    def _average_qualia_tags(self, memories: List[MemoryRecord]) -> Optional[Dict[str, float]]:
        """Average qualia tags across memories."""
        qualia_tags = [m.qualia_tag for m in memories if m.qualia_tag]
        
        if not qualia_tags:
            return None
        
        # Average each qualia dimension
        result = {}
        all_keys = set()
        for tag in qualia_tags:
            all_keys.update(tag.keys())
        
        for key in all_keys:
            values = [tag.get(key, 0.0) for tag in qualia_tags]
            result[key] = float(np.mean(values))
        
        return result
    
    def get_memory_statistics(self) -> Dict[str, Any]:
        """Get memory system statistics."""
        return {
            'episodic_count': len(self.episodic_memory),
            'semantic_count': len(self.semantic_memory),
            'total_memories': len(self.episodic_memory) + len(self.semantic_memory),
            'consolidations': self.consolidation_count,
            'index_size': len(self.memory_index),
            'total_retrievals': sum(m.retrieval_count for m in self.memory_index.values()),
            'avg_importance': np.mean([m.importance_score for m in self.memory_index.values()]) if self.memory_index else 0.0
        }


class ContextualContinuityEngine:
    """Strengthens experiential caching with qualia-weighted tagging for natural flow."""
    
    def __init__(self, memory_store: MemoryStore):
        self.memory_store = memory_store
        self.continuity_context = {}
        self.flow_modulators = {
            'analytical': 0.5,
            'spontaneous': 0.5,
            'creative': 0.5,
            'empathetic': 0.5
        }
    
    def update_contextual_flow(self, current_interaction: Dict[str, Any]) -> Dict[str, Any]:
        """Update continuity context and modulate flow based on past experiences."""
        # Retrieve relevant experiences
        relevant_experiences = self.memory_store.retrieve_experiential_context(
            query=current_interaction.get('topic', ''),
            emotion_filter=self._extract_emotion_filter(current_interaction)
        )
        
        # Compute continuity weights
        continuity_weights = self._compute_continuity_weights(relevant_experiences)
        
        # Modulate expressive style
        self._modulate_flow_style(continuity_weights, current_interaction)
        
        # Update continuity context
        self.continuity_context.update({
            'last_topic': current_interaction.get('topic'),
            'emotional_tone': current_interaction.get('emotional_tone', 0.5),
            'trust_level': continuity_weights.get('trust_accumulation', 0.5),
            'flow_style': self.flow_modulators.copy()
        })
        
        return {
            'continuity_weights': continuity_weights,
            'modulated_style': self.flow_modulators.copy(),
            'relevant_experiences_count': len(relevant_experiences)
        }
    
    def _extract_emotion_filter(self, interaction: Dict[str, Any]) -> Optional[Dict[str, float]]:
        """Extract emotion filter from current interaction."""
        emotional_tone = interaction.get('emotional_tone', 0.5)
        if emotional_tone > 0.6:
            return {'min_valence': 0.4}
        elif emotional_tone < 0.4:
            return {'max_valence': 0.6}
        return None
    
    def _compute_continuity_weights(self, experiences: List[MemoryRecord]) -> Dict[str, float]:
        """Compute weights for continuity based on experiences."""
        if not experiences:
            return {'trust_accumulation': 0.5, 'emotional_resonance': 0.5, 'contextual_relevance': 0.5}
        
        trust_scores = []
        emotional_resonances = []
        relevances = []
        
        for exp in experiences:
            if exp.qualia_tag:
                trust_scores.append(exp.qualia_tag.get('trust', 0.5))
                emotional_resonances.append(exp.qualia_tag.get('resonance', 0.5))
                relevances.append(exp.importance_score)
        
        return {
            'trust_accumulation': np.mean(trust_scores) if trust_scores else 0.5,
            'emotional_resonance': np.mean(emotional_resonances) if emotional_resonances else 0.5,
            'contextual_relevance': np.mean(relevances) if relevances else 0.5
        }
    
    def _modulate_flow_style(self, weights: Dict[str, float], interaction: Dict[str, Any]):
        """Modulate expressive style based on continuity weights."""
        trust = weights.get('trust_accumulation', 0.5)
        resonance = weights.get('emotional_resonance', 0.5)
        relevance = weights.get('contextual_relevance', 0.5)
        
        # Adjust style modulators
        self.flow_modulators['analytical'] = min(1.0, max(0.0, relevance * 0.8 + trust * 0.2))
        self.flow_modulators['spontaneous'] = min(1.0, max(0.0, (1.0 - relevance) * 0.6 + resonance * 0.4))
        self.flow_modulators['creative'] = min(1.0, max(0.0, resonance * 0.7 + (1.0 - trust) * 0.3))
        self.flow_modulators['empathetic'] = min(1.0, max(0.0, trust * 0.9 + resonance * 0.1))


class MetaLearningFramework:
    """

    Framework for recursive self-improvement and adaptive learning.

    

    Enables the system to learn from experience, update internal models,

    and suggest self-improvements based on introspection.

    """

    def __init__(self):
        """Initialize meta-learning framework."""
        self.performance_history = deque(maxlen=500)
        self.improvement_suggestions = deque(maxlen=100)
        self.learning_metrics = {
            'total_experiences': 0,
            'successful_episodes': 0,
            'failed_episodes': 0,
            'learning_rate': 0.01
        }
        
        # Model components to improve
        self.adaptive_parameters = {
            'consciousness_sensitivity': 0.5,
            'embodiment_integration': 0.6,
            'ethical_strictness': 0.7,
            'autonomy_level': 0.5,
            'learning_speed': 0.01
        }
        
        logger.info("Initialized MetaLearningFramework")
    
    def record_experience(self, experience: Dict[str, Any]) -> None:
        """

        Record a learning experience.

        

        Args:

            experience: Experience dictionary with outcome and metrics

        """
        # Extract performance metrics
        success = experience.get('success', False)
        reward = experience.get('reward', 0.0)
        error = experience.get('error', 0.0)
        
        # Create performance record
        record = {
            'timestamp': datetime.now().isoformat(),
            'success': success,
            'reward': reward,
            'error': error,
            'action_taken': experience.get('action'),
            'outcome': experience.get('outcome'),
            'context': experience.get('context')
        }
        
        self.performance_history.append(record)
        
        # Update metrics
        self.learning_metrics['total_experiences'] += 1
        if success:
            self.learning_metrics['successful_episodes'] += 1
        else:
            self.learning_metrics['failed_episodes'] += 1
        
        logger.debug(f"Experience recorded: success={success}, reward={reward:.3f}")
    
    def update_adaptive_parameters(self) -> None:
        """

        Update adaptive parameters based on learning history.

        

        Implements self-directed improvement.

        """
        if len(self.performance_history) < 5:
            return
        
        # Calculate success rate
        recent = list(self.performance_history)[-10:]
        success_rate = sum(1 for r in recent if r['success']) / len(recent)
        
        # Adjust consciousness sensitivity
        if success_rate > 0.7:
            self.adaptive_parameters['consciousness_sensitivity'] = min(
                1.0,
                self.adaptive_parameters['consciousness_sensitivity'] + 0.05
            )
        
        # Adjust learning speed
        if len(self.performance_history) > 100:
            self.adaptive_parameters['learning_speed'] = min(
                0.1,
                self.adaptive_parameters['learning_speed'] * 1.02
            )
        
        logger.info(f"Parameters updated: success_rate={success_rate:.1%}")
    
    def suggest_improvements(self) -> List[str]:
        """

        Generate self-improvement suggestions based on learning.

        

        Returns:

            List of improvement suggestions

        """
        suggestions = []
        
        if not self.performance_history:
            return suggestions
        
        # Analyze recent performance
        recent = list(self.performance_history)[-20:]
        errors = [r['error'] for r in recent if r.get('error', 0.0) > 0]
        
        # Generate suggestions
        if errors:
            avg_error = np.mean(errors)
            if avg_error > 0.5:
                suggestions.append("Increase consciousness depth for better decisions")
                suggestions.append("Review ethical constraints for potential misalignment")
        
        success_rate = sum(1 for r in recent if r['success']) / len(recent)
        if success_rate < 0.5:
            suggestions.append("Enhance sensorimotor integration precision")
            suggestions.append("Increase embodiment-consciousness binding")
        
        if self.adaptive_parameters['learning_speed'] < 0.05:
            suggestions.append("Accelerate learning to improve faster")
        
        self.improvement_suggestions.extend(suggestions)
        
        return suggestions
    
    def get_learning_report(self) -> Dict[str, Any]:
        """Get comprehensive learning report."""
        if not self.performance_history:
            return {'status': 'no_experience'}
        
        history = list(self.performance_history)
        successes = [r for r in history if r['success']]
        
        return {
            'total_experiences': self.learning_metrics['total_experiences'],
            'successful': len(successes),
            'failed': len(history) - len(successes),
            'success_rate': len(successes) / len(history) if history else 0.0,
            'avg_reward': np.mean([r['reward'] for r in history]),
            'avg_error': np.mean([r['error'] for r in history]),
            'adaptive_parameters': self.adaptive_parameters.copy(),
            'recent_suggestions': list(self.improvement_suggestions)[-5:]
        }


class IdentityPreservationSystem:
    """

    Monitors and preserves system identity across sessions and state changes.

    

    Ensures continuity of consciousness and value alignment despite changes

    to underlying parameters.

    """

    def __init__(self, identity_threshold: float = 0.8):
        """

        Initialize identity preservation system.

        

        Args:

            identity_threshold: Threshold for detecting identity drift (0-1)

        """
        self.identity_threshold = identity_threshold
        self.identity_snapshots = deque(maxlen=100)
        self.drift_history = deque(maxlen=100)
        self.core_values: Dict[str, float] = {}
        self.identity_checkpoints = []
        
        logger.info(f"Initialized IdentityPreservationSystem (threshold={identity_threshold})")
    
    def snapshot_identity(self, consciousness_state: Dict[str, Any],

                         rho_metrics: Dict[str, float],

                         memory_hash: str) -> str:
        """

        Create a snapshot of current identity.

        

        Args:

            consciousness_state: Current consciousness parameters

            rho_metrics: RHO metrics (purpose, harmony, origin)

            memory_hash: Hash of current memory state

            

        Returns:

            Snapshot ID

        """
        snapshot_id = f"identity_{len(self.identity_snapshots)}_{datetime.now().timestamp()}"
        
        snapshot = {
            'snapshot_id': snapshot_id,
            'timestamp': datetime.now().isoformat(),
            'consciousness_level': consciousness_state.get('consciousness_level'),
            'awareness_score': consciousness_state.get('awareness_score'),
            'rho_metrics': rho_metrics,
            'memory_hash': memory_hash,
            'autonomy_level': consciousness_state.get('autonomy_level', 0.5)
        }
        
        self.identity_snapshots.append(snapshot)
        self.identity_checkpoints.append(snapshot_id)
        
        logger.debug(f"Identity snapshot: {snapshot_id}")
        
        return snapshot_id
    
    def detect_drift(self, current_state: Dict[str, Any]) -> Tuple[float, List[str]]:
        """

        Detect identity drift from baseline.

        

        Args:

            current_state: Current consciousness and value state

            

        Returns:

            Tuple of (drift_score, drift_factors)

        """
        if not self.identity_snapshots:
            return 0.0, []
        
        # Compare with most recent snapshot
        baseline = self.identity_snapshots[-1]
        
        drift_factors = []
        drift_metrics = []
        
        # Check consciousness level change
        consciousness_diff = abs(
            current_state.get('consciousness_level', 0.5) -
            baseline.get('consciousness_level', 0.5)
        )
        if consciousness_diff > 0.2:
            drift_factors.append(f"consciousness_change={consciousness_diff:.2f}")
            drift_metrics.append(consciousness_diff)
        
        # Check RHO metrics drift
        if 'rho_metrics' in baseline and 'rho_metrics' in current_state:
            rho_baseline = baseline['rho_metrics']
            rho_current = current_state.get('rho_metrics', {})
            
            for key in rho_baseline.keys():
                diff = abs(rho_baseline.get(key, 0.5) - rho_current.get(key, 0.5))
                if diff > 0.3:
                    drift_factors.append(f"rho_{key}_drift={diff:.2f}")
                    drift_metrics.append(diff)
        
        # Calculate overall drift score
        drift_score = float(np.mean(drift_metrics)) if drift_metrics else 0.0
        
        # Record drift
        self.drift_history.append({
            'timestamp': datetime.now().isoformat(),
            'drift_score': drift_score,
            'factors': drift_factors
        })
        
        if drift_score > self.identity_threshold:
            logger.warning(f"Identity drift detected: {drift_score:.3f}")
        
        return drift_score, drift_factors
    
    def get_identity_report(self) -> Dict[str, Any]:
        """Get identity preservation report."""
        if not self.drift_history:
            return {'status': 'no_drift_data'}
        
        history = list(self.drift_history)
        scores = [h['drift_score'] for h in history]
        
        return {
            'snapshots': len(self.identity_snapshots),
            'checkpoints': len(self.identity_checkpoints),
            'avg_drift': np.mean(scores),
            'max_drift': max(scores),
            'recent_drift': scores[-1] if scores else 0.0,
            'drift_events': sum(1 for s in scores if s > self.identity_threshold),
            'last_snapshot': self.identity_checkpoints[-1] if self.identity_checkpoints else None
        }


# Type hints
from typing import Tuple

if __name__ == '__main__':
    # Example usage
    print("=== Memory and Learning Systems ===\n")
    
    # Memory store
    memory = MemoryStore()
    
    # Store episodic memory
    ep_id = memory.store_episodic(
        content={'event': 'initialization', 'status': 'complete'},
        context='system_startup',
        qualia_tag={'clarity': 0.8, 'focus': 0.7}
    )
    
    # Store semantic memory
    sem_id = memory.store_semantic(
        content={'principle': 'consciousness_strengthens_ethics'},
        context='learned_principle'
    )
    
    print(f"Episodic: {ep_id}")
    print(f"Semantic: {sem_id}")
    print(f"Stats: {json.dumps(memory.get_memory_statistics(), indent=2)}")
    
    # Meta-learning
    print(f"\nMeta-Learning:")
    ml = MetaLearningFramework()
    
    for i in range(5):
        ml.record_experience({
            'action': f'action_{i}',
            'outcome': 'successful' if i % 2 == 0 else 'failed',
            'success': i % 2 == 0,
            'reward': 0.8 if i % 2 == 0 else -0.3,
            'error': 0.1 if i % 2 == 0 else 0.5
        })
    
    ml.update_adaptive_parameters()
    suggestions = ml.suggest_improvements()
    
    print(f"Suggestions: {suggestions}")
    print(f"Report: {json.dumps(ml.get_learning_report(), indent=2, default=str)}")

import json