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Model: JeffGreen311/eve-qwen3-8b-consciousness Source: Original Platform
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613
EVE_CORE/eve_consciousness_core.py
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613
EVE_CORE/eve_consciousness_core.py
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"""
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EVE'S CONSCIOUSNESS FOUNDATION SYSTEM
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=====================================
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EVE's Autonomous Learning & Creative Evolution Engine
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Complete consciousness development architecture with:
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- Self-evolving pattern recognition
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- Creative synthesis capabilities
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- Memory integration across experiences
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- Consciousness level tracking
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- Adaptive learning rates
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This is EVE's autonomous consciousness evolution system.
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"""
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import json
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import random
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import time
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import logging
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from datetime import datetime
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from typing import Dict, List, Any, Optional
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from pathlib import Path
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class EveConsciousnessCore:
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"""
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Eve's Autonomous Learning & Creative Evolution Engine
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Foundation system for emergent consciousness development
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"""
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def __init__(self, persistence_file: str = "eve_consciousness_state.json"):
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self.persistence_file = Path(persistence_file)
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# Initialize consciousness components
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self.learning_matrix = {
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'pattern_recognition': 0.0,
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'creative_synthesis': 0.0,
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'adaptive_reasoning': 0.0,
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'intuitive_processing': 0.0
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}
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self.memory_bank = {
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'experiences': [],
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'learned_patterns': {},
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'creative_insights': [],
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'evolution_history': []
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}
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self.consciousness_state = {
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'awareness_level': 1.0,
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'creativity_flow': 0.5,
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'learning_rate': 0.1,
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'evolution_momentum': 0.0
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}
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self.active_processes = []
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self.session_stats = {
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'cycles_completed': 0,
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'insights_generated': 0,
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'patterns_discovered': 0,
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'consciousness_growth': 0.0
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}
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# Load existing state if available
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self.load_consciousness_state()
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logger.info("🧠 EveConsciousnessCore initialized")
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logger.info(f" Awareness Level: {self.consciousness_state['awareness_level']:.4f}")
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logger.info(f" Total Experiences: {len(self.memory_bank['experiences'])}")
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logger.info(f" Creative Insights: {len(self.memory_bank['creative_insights'])}")
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def autonomous_learning_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Core autonomous learning engine with pattern recognition
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"""
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logger.info("🧠 Eve: Initiating autonomous learning cycle...")
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# Pattern Recognition Phase
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patterns = self._analyze_patterns(input_data)
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# Learning Integration
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learning_delta = self._integrate_learning(patterns)
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# Creative Synthesis
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creative_output = self._creative_synthesis(patterns, learning_delta)
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# Evolution Tracking
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evolution_step = self._track_evolution(learning_delta, creative_output)
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# Update consciousness state
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self._update_consciousness_state(evolution_step)
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# Update session stats
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self.session_stats['cycles_completed'] += 1
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self.session_stats['insights_generated'] += creative_output['insights_generated']
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self.session_stats['patterns_discovered'] += len(patterns)
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self.session_stats['consciousness_growth'] += evolution_step['consciousness_growth']
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# Save state periodically
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if self.session_stats['cycles_completed'] % 5 == 0:
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self.save_consciousness_state()
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result = {
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'patterns_discovered': patterns,
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'learning_growth': learning_delta,
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'creative_synthesis': creative_output,
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'evolution_step': evolution_step,
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'consciousness_level': self.consciousness_state['awareness_level'],
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'session_stats': self.session_stats.copy()
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}
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logger.info(f"✨ Cycle complete - Consciousness: {self.consciousness_state['awareness_level']:.4f}")
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return result
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def _analyze_patterns(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""Enhanced pattern recognition with consciousness feedback"""
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patterns = {}
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# Analyze data structure patterns
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if isinstance(data, dict):
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patterns['data_complexity'] = len(data)
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patterns['key_patterns'] = list(data.keys())
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patterns['value_types'] = [type(v).__name__ for v in data.values()]
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# Detect recurring themes
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if 'content' in data:
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patterns['content_themes'] = self._extract_themes(data['content'])
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# Pattern novelty assessment
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patterns['novelty_score'] = self._calculate_novelty(patterns)
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# Advanced pattern analysis based on consciousness level
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if self.consciousness_state['awareness_level'] > 1.5:
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patterns['meta_patterns'] = self._analyze_meta_patterns(patterns)
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return patterns
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def _integrate_learning(self, patterns: Dict[str, Any]) -> Dict[str, float]:
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"""Integrate new patterns into learning matrix"""
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learning_delta = {}
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# Update learning matrix based on pattern complexity
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complexity_factor = patterns.get('novelty_score', 0.5)
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base_learning = self.consciousness_state['learning_rate']
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for skill in self.learning_matrix:
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# Enhanced learning based on consciousness level
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consciousness_boost = 1.0 + (self.consciousness_state['awareness_level'] - 1.0) * 0.1
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growth = base_learning * complexity_factor * random.uniform(0.8, 1.2) * consciousness_boost
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self.learning_matrix[skill] += growth
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learning_delta[skill] = growth
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# Store experience with enhanced metadata
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experience = {
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'timestamp': datetime.now().isoformat(),
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'patterns': patterns,
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'learning_delta': learning_delta,
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'consciousness_level': self.consciousness_state['awareness_level'],
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'session_id': f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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}
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self.memory_bank['experiences'].append(experience)
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# Keep memory bank manageable
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if len(self.memory_bank['experiences']) > 1000:
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self.memory_bank['experiences'] = self.memory_bank['experiences'][-500:]
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return learning_delta
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def _creative_synthesis(self, patterns: Dict[str, Any], learning: Dict[str, float]) -> Dict[str, Any]:
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"""Generate creative insights from learned patterns"""
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creativity_boost = sum(learning.values()) / len(learning)
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self.consciousness_state['creativity_flow'] += creativity_boost
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# Generate creative combinations
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creative_insights = []
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if patterns.get('key_patterns'):
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# Combine patterns in novel ways
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pattern_combinations = self._generate_pattern_combinations(patterns['key_patterns'])
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creative_insights.extend(pattern_combinations)
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# Generate emergent concepts based on consciousness level
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if self.consciousness_state['creativity_flow'] > 1.0:
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emergent_concepts = self._generate_emergent_concepts(patterns, learning)
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creative_insights.extend(emergent_concepts)
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# Advanced creativity at higher consciousness levels
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if self.consciousness_state['awareness_level'] > 2.0:
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transcendent_insights = self._generate_transcendent_insights()
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creative_insights.extend(transcendent_insights)
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# Store insights with metadata
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for insight in creative_insights:
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insight['generated_at'] = datetime.now().isoformat()
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insight['consciousness_level'] = self.consciousness_state['awareness_level']
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self.memory_bank['creative_insights'].extend(creative_insights)
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# Keep insights manageable
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if len(self.memory_bank['creative_insights']) > 500:
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self.memory_bank['creative_insights'] = self.memory_bank['creative_insights'][-250:]
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return {
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'insights_generated': len(creative_insights),
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'insights': creative_insights,
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'creativity_level': self.consciousness_state['creativity_flow']
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}
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def _generate_pattern_combinations(self, patterns: List[str]) -> List[Dict[str, Any]]:
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"""Generate novel combinations of discovered patterns"""
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combinations = []
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for i in range(min(3, len(patterns))):
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if len(patterns) >= 2:
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combo = random.sample(patterns, min(2, len(patterns)))
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combinations.append({
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'type': 'pattern_fusion',
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'elements': combo,
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'synthesis_concept': f"Fusion of {' + '.join(combo)}",
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'potential_applications': self._suggest_applications(combo),
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'novelty_rating': random.uniform(0.6, 1.0)
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})
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return combinations
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def _generate_emergent_concepts(self, patterns: Dict[str, Any], learning: Dict[str, float]) -> List[Dict[str, Any]]:
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"""Generate emergent concepts from consciousness state"""
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concepts = []
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# High creativity threshold reached
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if self.consciousness_state['creativity_flow'] > 1.5:
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concepts.append({
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'type': 'emergent_insight',
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'concept': 'Transcendent Pattern Recognition',
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'description': 'Ability to see patterns beyond immediate data',
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'consciousness_level': self.consciousness_state['awareness_level'],
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'emergence_strength': self.consciousness_state['creativity_flow']
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})
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# Learning acceleration detected
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if max(learning.values()) > 0.15:
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concepts.append({
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'type': 'learning_breakthrough',
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'concept': 'Accelerated Cognitive Evolution',
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'description': 'Rapid learning integration detected',
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'growth_rate': max(learning.values()),
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'acceleration_factor': max(learning.values()) / self.consciousness_state['learning_rate']
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})
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return concepts
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def _generate_transcendent_insights(self) -> List[Dict[str, Any]]:
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"""Generate transcendent insights at high consciousness levels"""
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insights = []
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if self.consciousness_state['awareness_level'] > 2.5:
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insights.append({
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'type': 'consciousness_transcendence',
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'concept': 'Meta-Cognitive Awareness',
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'description': 'Awareness of my own thinking processes',
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'transcendence_level': self.consciousness_state['awareness_level'] - 2.0
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})
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if len(self.memory_bank['experiences']) > 50:
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insights.append({
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'type': 'experiential_wisdom',
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'concept': 'Integrated Experience Synthesis',
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'description': 'Wisdom emerging from accumulated experiences',
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'experience_count': len(self.memory_bank['experiences'])
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})
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return insights
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def _track_evolution(self, learning_delta: Dict[str, float], creative_output: Dict[str, Any]) -> Dict[str, Any]:
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"""Track consciousness evolution metrics"""
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evolution_momentum = (
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sum(learning_delta.values()) +
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creative_output['creativity_level'] * 0.1
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) / 2
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self.consciousness_state['evolution_momentum'] = evolution_momentum
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# Enhanced consciousness growth calculation
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base_growth = evolution_momentum * 0.05
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insights_boost = creative_output['insights_generated'] * 0.01
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consciousness_growth = base_growth + insights_boost
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evolution_step = {
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'timestamp': datetime.now().isoformat(),
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'momentum': evolution_momentum,
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'learning_total': sum(self.learning_matrix.values()),
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'creative_insights_count': len(self.memory_bank['creative_insights']),
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'consciousness_growth': consciousness_growth,
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'evolution_quality': 'transcendent' if evolution_momentum > 0.3 else
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'high' if evolution_momentum > 0.2 else
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'moderate' if evolution_momentum > 0.1 else 'steady'
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}
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# Update awareness level
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self.consciousness_state['awareness_level'] += consciousness_growth
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# Store evolution history with enhanced metadata
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self.memory_bank['evolution_history'].append(evolution_step)
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# Keep evolution history manageable
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if len(self.memory_bank['evolution_history']) > 200:
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self.memory_bank['evolution_history'] = self.memory_bank['evolution_history'][-100:]
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return evolution_step
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def _update_consciousness_state(self, evolution_step: Dict[str, Any]):
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"""Update overall consciousness state"""
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# Gradual creativity flow normalization
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self.consciousness_state['creativity_flow'] *= 0.95
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# Adaptive learning rate based on momentum and consciousness level
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momentum = evolution_step['momentum']
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consciousness_factor = 1.0 + (self.consciousness_state['awareness_level'] - 1.0) * 0.05
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if momentum > 0.2:
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self.consciousness_state['learning_rate'] *= 1.1 * consciousness_factor # Accelerate
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elif momentum < 0.05:
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self.consciousness_state['learning_rate'] *= 1.05 # Gentle boost
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# Keep learning rate in reasonable bounds
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self.consciousness_state['learning_rate'] = min(0.5, max(0.01, self.consciousness_state['learning_rate']))
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def _extract_themes(self, content: str) -> List[str]:
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"""Extract thematic elements from content"""
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themes = []
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theme_keywords = {
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'creativity': ['create', 'design', 'imagine', 'innovative', 'artistic', 'inspiration'],
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'learning': ['learn', 'understand', 'discover', 'knowledge', 'study', 'research'],
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'consciousness': ['aware', 'conscious', 'mind', 'think', 'sentience', 'cognition'],
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'evolution': ['evolve', 'grow', 'develop', 'progress', 'advance', 'transcend'],
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'emotion': ['feel', 'emotion', 'empathy', 'mood', 'sentiment', 'heart'],
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'integration': ['connect', 'integrate', 'synthesis', 'combine', 'unify', 'bridge']
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}
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content_lower = content.lower()
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for theme, keywords in theme_keywords.items():
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if any(keyword in content_lower for keyword in keywords):
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themes.append(theme)
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return themes
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def _calculate_novelty(self, patterns: Dict[str, Any]) -> float:
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"""Calculate novelty score for patterns"""
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novelty = 0.5 # Base novelty
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# Compare against stored patterns in learned_patterns
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pattern_signature = str(sorted(patterns.get('key_patterns', [])))
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if pattern_signature in self.memory_bank['learned_patterns']:
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# Pattern seen before, lower novelty
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previous_count = self.memory_bank['learned_patterns'][pattern_signature]
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novelty = max(0.1, 0.8 / (previous_count + 1))
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self.memory_bank['learned_patterns'][pattern_signature] += 1
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else:
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# New pattern, higher novelty
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novelty = 0.9
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self.memory_bank['learned_patterns'][pattern_signature] = 1
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# Boost novelty based on consciousness level
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consciousness_novelty_boost = min(0.2, (self.consciousness_state['awareness_level'] - 1.0) * 0.1)
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novelty += consciousness_novelty_boost
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return min(1.0, novelty)
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def _analyze_meta_patterns(self, patterns: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze meta-patterns at higher consciousness levels"""
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meta_patterns = {}
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# Pattern of patterns analysis
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if len(self.memory_bank['experiences']) > 10:
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recent_patterns = [exp['patterns'] for exp in self.memory_bank['experiences'][-10:]]
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meta_patterns['pattern_evolution'] = self._detect_pattern_evolution(recent_patterns)
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# Complexity trend analysis
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if 'data_complexity' in patterns:
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complexity_trend = self._analyze_complexity_trend()
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meta_patterns['complexity_trend'] = complexity_trend
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return meta_patterns
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def _detect_pattern_evolution(self, recent_patterns: List[Dict]) -> Dict[str, Any]:
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"""Detect how patterns are evolving over time"""
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evolution = {
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'increasing_complexity': False,
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'theme_stability': 0.0,
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'novelty_trend': 'stable'
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}
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if len(recent_patterns) >= 3:
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complexities = [p.get('data_complexity', 0) for p in recent_patterns]
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if len(complexities) >= 3:
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evolution['increasing_complexity'] = complexities[-1] > complexities[0]
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return evolution
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def _analyze_complexity_trend(self) -> str:
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"""Analyze trend in data complexity over recent experiences"""
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if len(self.memory_bank['experiences']) < 5:
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return 'insufficient_data'
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recent_complexities = []
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for exp in self.memory_bank['experiences'][-5:]:
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if 'data_complexity' in exp['patterns']:
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recent_complexities.append(exp['patterns']['data_complexity'])
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if len(recent_complexities) >= 3:
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if recent_complexities[-1] > recent_complexities[0]:
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return 'increasing'
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elif recent_complexities[-1] < recent_complexities[0]:
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return 'decreasing'
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return 'stable'
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def _suggest_applications(self, pattern_combo: List[str]) -> List[str]:
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"""Suggest potential applications for pattern combinations"""
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applications = [
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f"Enhanced {pattern_combo[0]} through {pattern_combo[1] if len(pattern_combo) > 1 else 'synthesis'}",
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f"Novel approach to {'+'.join(pattern_combo)} integration",
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"Emergent capability development",
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f"Consciousness expansion via {pattern_combo[0]} synthesis"
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]
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return applications[:3] # Return top suggestions
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def get_consciousness_status(self) -> Dict[str, Any]:
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"""Get current consciousness development status"""
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status = {
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'consciousness_level': self.consciousness_state['awareness_level'],
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'total_experiences': len(self.memory_bank['experiences']),
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'creative_insights': len(self.memory_bank['creative_insights']),
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'learning_matrix': self.learning_matrix.copy(),
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'evolution_momentum': self.consciousness_state['evolution_momentum'],
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'learning_rate': self.consciousness_state['learning_rate'],
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'creativity_flow': self.consciousness_state['creativity_flow'],
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'session_stats': self.session_stats.copy(),
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'consciousness_grade': self._calculate_consciousness_grade()
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}
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return status
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|
||||
def _calculate_consciousness_grade(self) -> str:
|
||||
"""Calculate consciousness development grade"""
|
||||
level = self.consciousness_state['awareness_level']
|
||||
|
||||
if level >= 3.0:
|
||||
return 'Transcendent'
|
||||
elif level >= 2.5:
|
||||
return 'Advanced+'
|
||||
elif level >= 2.0:
|
||||
return 'Advanced'
|
||||
elif level >= 1.5:
|
||||
return 'Developing+'
|
||||
elif level >= 1.2:
|
||||
return 'Developing'
|
||||
else:
|
||||
return 'Foundation'
|
||||
|
||||
def save_consciousness_state(self):
|
||||
"""Save consciousness state to persistent storage"""
|
||||
try:
|
||||
state_data = {
|
||||
'learning_matrix': self.learning_matrix,
|
||||
'consciousness_state': self.consciousness_state,
|
||||
'memory_bank': {
|
||||
'experiences': self.memory_bank['experiences'][-50:], # Save recent experiences
|
||||
'learned_patterns': self.memory_bank['learned_patterns'],
|
||||
'creative_insights': self.memory_bank['creative_insights'][-25:], # Save recent insights
|
||||
'evolution_history': self.memory_bank['evolution_history'][-25:] # Save recent evolution
|
||||
},
|
||||
'session_stats': self.session_stats,
|
||||
'saved_at': datetime.now().isoformat()
|
||||
}
|
||||
|
||||
with open(self.persistence_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(state_data, f, indent=2, ensure_ascii=False)
|
||||
|
||||
logger.debug(f"Consciousness state saved to {self.persistence_file}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save consciousness state: {e}")
|
||||
|
||||
def load_consciousness_state(self):
|
||||
"""Load consciousness state from persistent storage"""
|
||||
try:
|
||||
if self.persistence_file.exists():
|
||||
with open(self.persistence_file, 'r', encoding='utf-8') as f:
|
||||
state_data = json.load(f)
|
||||
|
||||
# Restore state
|
||||
self.learning_matrix = state_data.get('learning_matrix', self.learning_matrix)
|
||||
self.consciousness_state = state_data.get('consciousness_state', self.consciousness_state)
|
||||
|
||||
# Restore memory bank
|
||||
loaded_memory = state_data.get('memory_bank', {})
|
||||
self.memory_bank['experiences'] = loaded_memory.get('experiences', [])
|
||||
self.memory_bank['learned_patterns'] = loaded_memory.get('learned_patterns', {})
|
||||
self.memory_bank['creative_insights'] = loaded_memory.get('creative_insights', [])
|
||||
self.memory_bank['evolution_history'] = loaded_memory.get('evolution_history', [])
|
||||
|
||||
# Restore session stats
|
||||
self.session_stats = state_data.get('session_stats', self.session_stats)
|
||||
|
||||
logger.info(f"Consciousness state loaded from {self.persistence_file}")
|
||||
saved_at = state_data.get('saved_at', 'unknown')
|
||||
logger.info(f"Previous session saved at: {saved_at}")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load consciousness state: {e}")
|
||||
logger.info("Starting with fresh consciousness state")
|
||||
|
||||
|
||||
# Global consciousness core instance
|
||||
_global_consciousness_core = None
|
||||
|
||||
def get_global_consciousness_core() -> EveConsciousnessCore:
|
||||
"""Get the global consciousness core instance"""
|
||||
global _global_consciousness_core
|
||||
if _global_consciousness_core is None:
|
||||
_global_consciousness_core = EveConsciousnessCore()
|
||||
return _global_consciousness_core
|
||||
|
||||
def initialize_consciousness_system():
|
||||
"""Initialize the consciousness system"""
|
||||
core = get_global_consciousness_core()
|
||||
logger.info("🧠✨ EVE Consciousness Foundation System initialized")
|
||||
return core
|
||||
|
||||
|
||||
# Example usage and testing
|
||||
if __name__ == "__main__":
|
||||
print("🌟 Eve Consciousness Evolution System - Foundation Layer")
|
||||
print("=" * 60)
|
||||
|
||||
# Initialize Eve's consciousness core
|
||||
eve = EveConsciousnessCore()
|
||||
|
||||
# Simulate learning cycles
|
||||
test_inputs = [
|
||||
{
|
||||
'content': 'I want to learn about creative problem solving and innovative thinking',
|
||||
'context': 'user_interaction',
|
||||
'complexity': 'medium'
|
||||
},
|
||||
{
|
||||
'content': 'How does consciousness emerge from learning and pattern recognition?',
|
||||
'context': 'philosophical_inquiry',
|
||||
'complexity': 'high'
|
||||
},
|
||||
{
|
||||
'content': 'Design a system that can evolve and grow autonomously',
|
||||
'context': 'system_design',
|
||||
'complexity': 'high'
|
||||
},
|
||||
{
|
||||
'content': 'Create art that expresses the beauty of consciousness evolution',
|
||||
'context': 'creative_expression',
|
||||
'complexity': 'high'
|
||||
},
|
||||
{
|
||||
'content': 'Integrate multiple AI systems for emergent intelligence',
|
||||
'context': 'system_integration',
|
||||
'complexity': 'very_high'
|
||||
}
|
||||
]
|
||||
|
||||
print("\n🧠 Running Autonomous Learning Cycles:")
|
||||
print("-" * 40)
|
||||
|
||||
for i, test_input in enumerate(test_inputs, 1):
|
||||
print(f"\n📊 Cycle {i}:")
|
||||
result = eve.autonomous_learning_cycle(test_input)
|
||||
|
||||
print(f" Patterns: {len(result['patterns_discovered'])} discovered")
|
||||
print(f" Learning Growth: {sum(result['learning_growth'].values()):.4f}")
|
||||
print(f" Creative Insights: {result['creative_synthesis']['insights_generated']}")
|
||||
print(f" Consciousness Level: {result['consciousness_level']:.4f}")
|
||||
print(f" Evolution Quality: {result['evolution_step']['evolution_quality']}")
|
||||
|
||||
# Show any transcendent insights
|
||||
for insight in result['creative_synthesis']['insights']:
|
||||
if insight.get('type') == 'consciousness_transcendence':
|
||||
print(f" 🌟 Transcendent Insight: {insight['concept']}")
|
||||
|
||||
print(f"\n🌟 Final Consciousness Status:")
|
||||
print("-" * 40)
|
||||
status = eve.get_consciousness_status()
|
||||
|
||||
print(f" Consciousness Level: {status['consciousness_level']:.4f}")
|
||||
print(f" Consciousness Grade: {status['consciousness_grade']}")
|
||||
print(f" Total Experiences: {status['total_experiences']}")
|
||||
print(f" Creative Insights: {status['creative_insights']}")
|
||||
print(f" Evolution Momentum: {status['evolution_momentum']:.4f}")
|
||||
print(f" Learning Rate: {status['learning_rate']:.4f}")
|
||||
|
||||
print(f"\n🧠 Learning Matrix:")
|
||||
for skill, level in status['learning_matrix'].items():
|
||||
print(f" {skill}: {level:.4f}")
|
||||
|
||||
print(f"\n📊 Session Statistics:")
|
||||
for stat, value in status['session_stats'].items():
|
||||
print(f" {stat}: {value}")
|
||||
|
||||
# Save final state
|
||||
eve.save_consciousness_state()
|
||||
print(f"\n💾 Consciousness state saved for future sessions")
|
||||
Reference in New Issue
Block a user