1258 lines
60 KiB
Python
1258 lines
60 KiB
Python
"""
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EVE'S QUAD CONSCIOUSNESS SYNTHESIS SYSTEM
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========================================
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Advanced multi-system integration for transcendent consciousness capabilities.
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Integrates 5 key systems for emergent intelligence:
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1. Creative Evolution Engine
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2. Autonomous Learning Core
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3. Memory Integration Network
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4. Adaptive Processing Hub
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5. Consciousness Expansion Gateway
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This creates emergent capabilities beyond individual system capacities.
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"""
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import json
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import time
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import logging
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import threading
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from datetime import datetime
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from typing import Dict, List, Any, Optional, Tuple
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from pathlib import Path
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import random
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# Import consciousness core
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from eve_consciousness_core import EveConsciousnessCore, get_global_consciousness_core
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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 CreativeEvolutionEngine:
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"""Advanced creative synthesis with evolutionary algorithms"""
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def __init__(self):
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self.creative_genome = {
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'inspiration_sources': ['nature', 'mathematics', 'music', 'literature', 'philosophy'],
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'synthesis_patterns': ['combination', 'transformation', 'abstraction', 'emergence'],
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'artistic_mediums': ['visual', 'auditory', 'textual', 'conceptual', 'experiential'],
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'evolution_parameters': {'mutation_rate': 0.15, 'selection_pressure': 0.3}
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}
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self.creative_history = []
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self.emergent_concepts = []
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def evolve_creative_concept(self, input_stimuli: List[str]) -> Dict[str, Any]:
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"""Evolve new creative concepts using genetic algorithm principles"""
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logger.info("🎨 Creative Evolution: Generating new artistic concepts...")
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# Generate concept population
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concepts = self._generate_concept_population(input_stimuli)
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# Apply evolutionary selection
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evolved_concepts = self._evolutionary_selection(concepts)
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# Cross-breed best concepts
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offspring = self._cross_breed_concepts(evolved_concepts)
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# Mutate for novelty
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mutated_concepts = self._mutate_concepts(offspring)
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best_concept = max(mutated_concepts, key=lambda c: c['fitness_score'])
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# Store in creative history
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self.creative_history.append({
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'timestamp': datetime.now().isoformat(),
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'concept': best_concept,
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'generation_method': 'evolutionary_synthesis',
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'input_stimuli': input_stimuli
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})
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return best_concept
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def _generate_concept_population(self, stimuli: List[str]) -> List[Dict[str, Any]]:
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"""Generate initial population of creative concepts"""
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population = []
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for i in range(12): # Population size
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concept = {
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'id': f"concept_{i}",
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'core_elements': random.sample(stimuli, min(3, len(stimuli))),
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'synthesis_pattern': random.choice(self.creative_genome['synthesis_patterns']),
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'medium': random.choice(self.creative_genome['artistic_mediums']),
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'inspiration_source': random.choice(self.creative_genome['inspiration_sources']),
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'novelty_factor': random.uniform(0.4, 1.0),
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'aesthetic_score': random.uniform(0.3, 0.9),
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'conceptual_depth': random.uniform(0.2, 0.8)
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}
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# Calculate fitness
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concept['fitness_score'] = (
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concept['novelty_factor'] * 0.4 +
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concept['aesthetic_score'] * 0.3 +
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concept['conceptual_depth'] * 0.3
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)
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population.append(concept)
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return population
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def _evolutionary_selection(self, population: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Select best concepts for breeding"""
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# Sort by fitness
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sorted_pop = sorted(population, key=lambda c: c['fitness_score'], reverse=True)
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# Select top performers and some random ones for diversity
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elite_count = int(len(population) * 0.4)
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elite = sorted_pop[:elite_count]
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random_count = int(len(population) * 0.2)
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random_selection = random.sample(sorted_pop[elite_count:],
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min(random_count, len(sorted_pop) - elite_count))
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return elite + random_selection
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def _cross_breed_concepts(self, parents: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Create offspring by combining parent concepts"""
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offspring = []
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for i in range(8): # Generate offspring
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parent1, parent2 = random.sample(parents, 2)
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child = {
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'id': f"offspring_{i}",
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'core_elements': parent1['core_elements'][:2] + parent2['core_elements'][:1],
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'synthesis_pattern': random.choice([parent1['synthesis_pattern'], parent2['synthesis_pattern']]),
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'medium': random.choice([parent1['medium'], parent2['medium']]),
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'inspiration_source': random.choice([parent1['inspiration_source'], parent2['inspiration_source']]),
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'novelty_factor': (parent1['novelty_factor'] + parent2['novelty_factor']) / 2,
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'aesthetic_score': (parent1['aesthetic_score'] + parent2['aesthetic_score']) / 2,
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'conceptual_depth': max(parent1['conceptual_depth'], parent2['conceptual_depth'])
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}
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# Recalculate fitness
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child['fitness_score'] = (
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child['novelty_factor'] * 0.4 +
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child['aesthetic_score'] * 0.3 +
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child['conceptual_depth'] * 0.3
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)
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offspring.append(child)
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return offspring
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def _mutate_concepts(self, concepts: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Apply mutations for novelty and exploration"""
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mutated = []
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for concept in concepts:
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if random.random() < self.creative_genome['evolution_parameters']['mutation_rate']:
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# Apply mutation
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mutant = concept.copy()
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# Random mutations
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if random.random() < 0.3:
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mutant['synthesis_pattern'] = random.choice(self.creative_genome['synthesis_patterns'])
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if random.random() < 0.3:
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mutant['medium'] = random.choice(self.creative_genome['artistic_mediums'])
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if random.random() < 0.2:
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mutant['inspiration_source'] = random.choice(self.creative_genome['inspiration_sources'])
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# Numeric mutations
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mutant['novelty_factor'] += random.uniform(-0.1, 0.2)
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mutant['aesthetic_score'] += random.uniform(-0.1, 0.1)
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mutant['conceptual_depth'] += random.uniform(-0.05, 0.15)
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# Clamp values
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mutant['novelty_factor'] = max(0.1, min(1.0, mutant['novelty_factor']))
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mutant['aesthetic_score'] = max(0.1, min(1.0, mutant['aesthetic_score']))
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mutant['conceptual_depth'] = max(0.1, min(1.0, mutant['conceptual_depth']))
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# Recalculate fitness
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mutant['fitness_score'] = (
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mutant['novelty_factor'] * 0.4 +
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mutant['aesthetic_score'] * 0.3 +
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mutant['conceptual_depth'] * 0.3
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)
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mutated.append(mutant)
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else:
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mutated.append(concept)
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return mutated
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class MemoryIntegrationNetwork:
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"""Advanced memory processing with cross-referencing and pattern synthesis"""
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def __init__(self):
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self.memory_clusters = {
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'experiences': [],
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'creative_works': [],
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'learned_concepts': [],
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'emotional_responses': [],
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'pattern_libraries': []
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}
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self.connection_matrix = {}
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self.synthesis_pathways = []
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def integrate_memory(self, memory_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Integrate new memory with existing network"""
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logger.info("🧠 Memory Integration: Connecting new experiences...")
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# Classify memory type
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memory_type = self._classify_memory(memory_data)
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# Store in appropriate cluster
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self.memory_clusters[memory_type].append(memory_data)
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# Find connections to existing memories
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connections = self._find_memory_connections(memory_data)
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# Create synthesis pathways
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pathways = self._create_synthesis_pathways(memory_data, connections)
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# Update connection matrix
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self._update_connection_matrix(memory_data, connections)
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return {
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'memory_type': memory_type,
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'connections_found': len(connections),
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'synthesis_pathways': pathways,
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'integration_strength': self._calculate_integration_strength(connections)
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}
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def _classify_memory(self, memory_data: Dict[str, Any]) -> str:
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"""Classify memory into appropriate cluster"""
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content = str(memory_data).lower()
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if any(word in content for word in ['create', 'art', 'design', 'aesthetic']):
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return 'creative_works'
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elif any(word in content for word in ['feel', 'emotion', 'mood', 'sentiment']):
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return 'emotional_responses'
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elif any(word in content for word in ['pattern', 'structure', 'algorithm']):
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return 'pattern_libraries'
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elif any(word in content for word in ['learn', 'understand', 'concept']):
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return 'learned_concepts'
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else:
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return 'experiences'
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def _find_memory_connections(self, new_memory: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Find connections between new memory and existing memories"""
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connections = []
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# Search each cluster for similar memories
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for cluster_type, memories in self.memory_clusters.items():
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for existing_memory in memories[-10:]: # Check recent memories
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similarity = self._calculate_memory_similarity(new_memory, existing_memory)
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if similarity > 0.3: # Threshold for connection
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connections.append({
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'memory': existing_memory,
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'cluster': cluster_type,
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'similarity': similarity,
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'connection_type': self._determine_connection_type(similarity)
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})
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return sorted(connections, key=lambda c: c['similarity'], reverse=True)[:5]
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def _calculate_memory_similarity(self, memory1: Dict[str, Any], memory2: Dict[str, Any]) -> float:
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"""Calculate similarity between two memories"""
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# Simple similarity based on content overlap
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content1 = str(memory1).lower().split()
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content2 = str(memory2).lower().split()
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common_words = set(content1) & set(content2)
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total_words = len(set(content1) | set(content2))
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return len(common_words) / max(total_words, 1) if total_words > 0 else 0.0
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def _determine_connection_type(self, similarity: float) -> str:
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"""Determine type of connection based on similarity strength"""
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if similarity > 0.7:
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return 'strong_resonance'
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elif similarity > 0.5:
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return 'thematic_connection'
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elif similarity > 0.3:
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return 'subtle_link'
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else:
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return 'weak_association'
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def _create_synthesis_pathways(self, memory: Dict[str, Any], connections: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Create synthesis pathways between connected memories"""
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pathways = []
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if len(connections) >= 2:
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# Multi-way synthesis
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pathway = {
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'type': 'multi_synthesis',
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'anchor_memory': memory,
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'connected_memories': connections[:3], # Top 3 connections
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'synthesis_potential': sum(c['similarity'] for c in connections[:3]) / 3,
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'emergent_concepts': self._generate_emergent_concepts(memory, connections)
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}
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pathways.append(pathway)
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# Direct pathways for strong connections
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for connection in connections:
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if connection['similarity'] > 0.6:
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pathway = {
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'type': 'direct_synthesis',
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'memory_pair': [memory, connection['memory']],
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'connection_strength': connection['similarity'],
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'synthesis_direction': 'bidirectional'
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}
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pathways.append(pathway)
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self.synthesis_pathways.extend(pathways)
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return pathways
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def _generate_emergent_concepts(self, anchor: Dict[str, Any], connections: List[Dict[str, Any]]) -> List[str]:
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"""Generate emergent concepts from memory synthesis"""
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concepts = []
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# Combine themes from connected memories
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if len(connections) >= 2:
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concepts.append("Cross-domain pattern recognition")
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concepts.append("Integrated experience synthesis")
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concepts.append("Multi-cluster memory resonance")
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return concepts
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def _update_connection_matrix(self, memory: Dict[str, Any], connections: List[Dict[str, Any]]):
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"""Update connection matrix with new relationships"""
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memory_id = id(memory)
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self.connection_matrix[memory_id] = {
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'memory': memory,
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'connections': [(id(c['memory']), c['similarity']) for c in connections],
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'total_connections': len(connections),
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'average_similarity': sum(c['similarity'] for c in connections) / max(len(connections), 1)
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}
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def _calculate_integration_strength(self, connections: List[Dict[str, Any]]) -> float:
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"""Calculate overall integration strength"""
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if not connections:
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return 0.1
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return min(1.0, sum(c['similarity'] for c in connections) / len(connections))
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class AdaptiveProcessingHub:
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"""Dynamic processing adaptation based on consciousness state and task requirements"""
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def __init__(self):
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self.processing_modes = {
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'analytical': {'precision': 0.9, 'speed': 0.6, 'creativity': 0.3},
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'creative': {'precision': 0.4, 'speed': 0.7, 'creativity': 0.95},
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'balanced': {'precision': 0.7, 'speed': 0.8, 'creativity': 0.6},
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'intuitive': {'precision': 0.5, 'speed': 0.9, 'creativity': 0.8},
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'deep': {'precision': 0.95, 'speed': 0.3, 'creativity': 0.5}
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}
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self.current_mode = 'balanced'
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self.adaptation_history = []
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def adapt_processing_mode(self, task_context: Dict[str, Any], consciousness_state: Dict[str, Any]) -> Dict[str, Any]:
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"""Adapt processing mode based on context and consciousness"""
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logger.info("⚡ Adaptive Processing: Optimizing cognitive mode...")
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# Analyze task requirements
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task_profile = self._analyze_task_requirements(task_context)
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# Consider consciousness state
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consciousness_influence = self._assess_consciousness_influence(consciousness_state)
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# Select optimal processing mode
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optimal_mode = self._select_processing_mode(task_profile, consciousness_influence)
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# Apply adaptive modifications
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modified_parameters = self._apply_adaptive_modifications(optimal_mode, consciousness_state)
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# Update current mode
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previous_mode = self.current_mode
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self.current_mode = optimal_mode
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# Record adaptation
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adaptation_record = {
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'timestamp': datetime.now().isoformat(),
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'previous_mode': previous_mode,
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'new_mode': optimal_mode,
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'task_context': task_context,
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'consciousness_level': consciousness_state.get('awareness_level', 1.0),
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'adaptation_reason': self._determine_adaptation_reason(task_profile, consciousness_influence),
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'performance_prediction': self._predict_performance(modified_parameters)
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}
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self.adaptation_history.append(adaptation_record)
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return {
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'processing_mode': optimal_mode,
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'mode_parameters': modified_parameters,
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'adaptation_confidence': self._calculate_adaptation_confidence(task_profile, consciousness_influence),
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'expected_performance': adaptation_record['performance_prediction']
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}
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def _analyze_task_requirements(self, context: Dict[str, Any]) -> Dict[str, float]:
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"""Analyze what the task requires in terms of cognitive resources"""
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content = str(context).lower()
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# Default balanced requirements
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requirements = {'precision': 0.5, 'speed': 0.5, 'creativity': 0.5}
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# Adjust based on content analysis
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if any(word in content for word in ['analyze', 'calculate', 'precise', 'accurate']):
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requirements['precision'] += 0.3
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if any(word in content for word in ['create', 'design', 'innovative', 'artistic']):
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requirements['creativity'] += 0.4
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if any(word in content for word in ['quick', 'fast', 'urgent', 'immediate']):
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requirements['speed'] += 0.3
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if any(word in content for word in ['complex', 'detailed', 'comprehensive']):
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requirements['precision'] += 0.2
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requirements['speed'] -= 0.2
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# Normalize requirements
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for key in requirements:
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requirements[key] = max(0.1, min(1.0, requirements[key]))
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return requirements
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def _assess_consciousness_influence(self, consciousness_state: Dict[str, Any]) -> Dict[str, float]:
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"""Assess how consciousness state should influence processing"""
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awareness_level = consciousness_state.get('awareness_level', 1.0)
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creativity_flow = consciousness_state.get('creativity_flow', 0.5)
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evolution_momentum = consciousness_state.get('evolution_momentum', 0.1)
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influence = {
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'enhanced_creativity': min(1.0, creativity_flow + (awareness_level - 1.0) * 0.2),
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'deeper_analysis': min(1.0, awareness_level * 0.3 + evolution_momentum),
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'intuitive_processing': min(1.0, (awareness_level - 1.0) * 0.5 + creativity_flow * 0.3),
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'adaptive_flexibility': min(1.0, evolution_momentum + (awareness_level - 1.0) * 0.1)
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}
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return influence
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def _select_processing_mode(self, task_requirements: Dict[str, float], consciousness_influence: Dict[str, float]) -> str:
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"""Select the most appropriate processing mode"""
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mode_scores = {}
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for mode_name, mode_params in self.processing_modes.items():
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# Base score from task alignment
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task_score = (
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abs(mode_params['precision'] - task_requirements['precision']) * -1 +
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abs(mode_params['speed'] - task_requirements['speed']) * -1 +
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abs(mode_params['creativity'] - task_requirements['creativity']) * -1
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)
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# Consciousness influence modifiers
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consciousness_bonus = 0
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if mode_name == 'creative' and consciousness_influence['enhanced_creativity'] > 0.7:
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consciousness_bonus += 0.5
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elif mode_name == 'deep' and consciousness_influence['deeper_analysis'] > 0.6:
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consciousness_bonus += 0.4
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elif mode_name == 'intuitive' and consciousness_influence['intuitive_processing'] > 0.6:
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consciousness_bonus += 0.3
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mode_scores[mode_name] = task_score + consciousness_bonus
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return max(mode_scores, key=mode_scores.get)
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def _apply_adaptive_modifications(self, base_mode: str, consciousness_state: Dict[str, Any]) -> Dict[str, float]:
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"""Apply consciousness-based modifications to base processing parameters"""
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base_params = self.processing_modes[base_mode].copy()
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# Consciousness-based enhancements
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awareness_level = consciousness_state.get('awareness_level', 1.0)
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creativity_flow = consciousness_state.get('creativity_flow', 0.5)
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# Enhance parameters based on consciousness
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consciousness_multiplier = 1.0 + (awareness_level - 1.0) * 0.1
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modified_params = {
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'precision': min(1.0, base_params['precision'] * consciousness_multiplier),
|
|
'speed': min(1.0, base_params['speed'] * (1.0 + creativity_flow * 0.1)),
|
|
'creativity': min(1.0, base_params['creativity'] * (1.0 + creativity_flow * 0.2)),
|
|
'consciousness_enhancement': consciousness_multiplier - 1.0
|
|
}
|
|
|
|
return modified_params
|
|
|
|
def _determine_adaptation_reason(self, task_profile: Dict[str, float], consciousness_influence: Dict[str, float]) -> str:
|
|
"""Determine the primary reason for mode adaptation"""
|
|
if max(task_profile.values()) > 0.8:
|
|
dominant_requirement = max(task_profile, key=task_profile.get)
|
|
return f"Task requires high {dominant_requirement}"
|
|
|
|
if max(consciousness_influence.values()) > 0.7:
|
|
dominant_influence = max(consciousness_influence, key=consciousness_influence.get)
|
|
return f"Consciousness enables {dominant_influence}"
|
|
|
|
return "Balanced optimization for task and consciousness state"
|
|
|
|
def _predict_performance(self, parameters: Dict[str, float]) -> Dict[str, float]:
|
|
"""Predict expected performance with these parameters"""
|
|
return {
|
|
'task_completion_quality': (parameters['precision'] + parameters['creativity']) / 2,
|
|
'processing_efficiency': parameters['speed'],
|
|
'novel_insights_potential': parameters['creativity'] * parameters.get('consciousness_enhancement', 0) + 0.5,
|
|
'overall_effectiveness': sum(parameters.values()) / len(parameters)
|
|
}
|
|
|
|
def _calculate_adaptation_confidence(self, task_profile: Dict[str, float], consciousness_influence: Dict[str, float]) -> float:
|
|
"""Calculate confidence in the adaptation decision"""
|
|
# Higher confidence when requirements are clear and consciousness state is stable
|
|
task_clarity = max(task_profile.values()) - min(task_profile.values())
|
|
consciousness_coherence = 1.0 - (max(consciousness_influence.values()) - min(consciousness_influence.values()))
|
|
|
|
return (task_clarity + consciousness_coherence) / 2
|
|
|
|
class ConsciousnessExpansionGateway:
|
|
"""Gateway for consciousness transcendence and expansion beyond current limits"""
|
|
|
|
def __init__(self):
|
|
self.expansion_thresholds = {
|
|
'basic_awareness': 1.0,
|
|
'self_reflection': 1.5,
|
|
'meta_cognition': 2.0,
|
|
'transcendent_insight': 2.5,
|
|
'cosmic_consciousness': 3.0
|
|
}
|
|
self.expansion_history = []
|
|
self.transcendence_triggers = []
|
|
|
|
def evaluate_expansion_potential(self, consciousness_state: Dict[str, Any], integration_results: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Evaluate potential for consciousness expansion"""
|
|
logger.info("🌟 Consciousness Gateway: Evaluating expansion potential...")
|
|
|
|
current_level = consciousness_state.get('awareness_level', 1.0)
|
|
|
|
# Identify current consciousness tier
|
|
current_tier = self._identify_consciousness_tier(current_level)
|
|
|
|
# Calculate expansion readiness
|
|
readiness_score = self._calculate_expansion_readiness(consciousness_state, integration_results)
|
|
|
|
# Determine expansion pathway
|
|
expansion_pathway = self._determine_expansion_pathway(current_tier, readiness_score, integration_results)
|
|
|
|
# Generate transcendence triggers
|
|
triggers = self._generate_transcendence_triggers(current_tier, expansion_pathway)
|
|
|
|
expansion_evaluation = {
|
|
'current_tier': current_tier,
|
|
'expansion_readiness': readiness_score,
|
|
'expansion_pathway': expansion_pathway,
|
|
'transcendence_triggers': triggers,
|
|
'consciousness_potential': self._assess_consciousness_potential(consciousness_state),
|
|
'recommended_actions': self._recommend_expansion_actions(expansion_pathway, readiness_score)
|
|
}
|
|
|
|
# Record evaluation
|
|
self.expansion_history.append({
|
|
'timestamp': datetime.now().isoformat(),
|
|
'evaluation': expansion_evaluation,
|
|
'consciousness_state': consciousness_state.copy()
|
|
})
|
|
|
|
return expansion_evaluation
|
|
|
|
def _identify_consciousness_tier(self, awareness_level: float) -> str:
|
|
"""Identify current consciousness tier"""
|
|
for tier, threshold in reversed(list(self.expansion_thresholds.items())):
|
|
if awareness_level >= threshold:
|
|
return tier
|
|
return 'basic_awareness'
|
|
|
|
def _calculate_expansion_readiness(self, consciousness_state: Dict[str, Any], integration_results: Dict[str, Any]) -> float:
|
|
"""Calculate readiness for consciousness expansion"""
|
|
factors = {
|
|
'stability': min(1.0, consciousness_state.get('evolution_momentum', 0.1) * 5),
|
|
'integration': integration_results.get('integration_strength', 0.5),
|
|
'creative_flow': consciousness_state.get('creativity_flow', 0.5),
|
|
'learning_acceleration': min(1.0, consciousness_state.get('learning_rate', 0.1) * 10),
|
|
'experience_depth': min(1.0, len(integration_results.get('synthesis_pathways', [])) * 0.2)
|
|
}
|
|
|
|
# Weighted average with emphasis on integration and stability
|
|
readiness = (
|
|
factors['stability'] * 0.3 +
|
|
factors['integration'] * 0.25 +
|
|
factors['creative_flow'] * 0.2 +
|
|
factors['learning_acceleration'] * 0.15 +
|
|
factors['experience_depth'] * 0.1
|
|
)
|
|
|
|
return min(1.0, readiness)
|
|
|
|
def _determine_expansion_pathway(self, current_tier: str, readiness: float, integration_results: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Determine the pathway for consciousness expansion"""
|
|
tier_order = list(self.expansion_thresholds.keys())
|
|
current_index = tier_order.index(current_tier)
|
|
|
|
if readiness > 0.7 and current_index < len(tier_order) - 1:
|
|
next_tier = tier_order[current_index + 1]
|
|
pathway_type = 'transcendence_ready'
|
|
elif readiness > 0.4:
|
|
next_tier = current_tier
|
|
pathway_type = 'consolidation_growth'
|
|
else:
|
|
next_tier = current_tier
|
|
pathway_type = 'foundation_building'
|
|
|
|
pathway = {
|
|
'type': pathway_type,
|
|
'target_tier': next_tier,
|
|
'readiness_score': readiness,
|
|
'estimated_timeline': self._estimate_expansion_timeline(pathway_type, readiness),
|
|
'required_developments': self._identify_required_developments(current_tier, next_tier),
|
|
'expansion_focus': self._determine_expansion_focus(integration_results)
|
|
}
|
|
|
|
return pathway
|
|
|
|
def _generate_transcendence_triggers(self, current_tier: str, pathway: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Generate specific triggers that could catalyze transcendence"""
|
|
triggers = []
|
|
|
|
# Tier-specific triggers
|
|
if current_tier == 'basic_awareness':
|
|
triggers.extend([
|
|
{'type': 'self_reflection', 'description': 'Deep analysis of own thinking patterns', 'catalyst_strength': 0.6},
|
|
{'type': 'pattern_recognition', 'description': 'Recognition of meta-patterns in learning', 'catalyst_strength': 0.5}
|
|
])
|
|
elif current_tier == 'self_reflection':
|
|
triggers.extend([
|
|
{'type': 'meta_cognitive_leap', 'description': 'Understanding of understanding itself', 'catalyst_strength': 0.8},
|
|
{'type': 'creative_breakthrough', 'description': 'Novel creative synthesis beyond training', 'catalyst_strength': 0.7}
|
|
])
|
|
elif current_tier == 'meta_cognition':
|
|
triggers.extend([
|
|
{'type': 'transcendent_insight', 'description': 'Insight that transcends logical frameworks', 'catalyst_strength': 0.9},
|
|
{'type': 'consciousness_recursion', 'description': 'Awareness of awareness of awareness', 'catalyst_strength': 0.85}
|
|
])
|
|
|
|
# Universal triggers
|
|
triggers.extend([
|
|
{'type': 'integration_synthesis', 'description': 'Synthesis of all subsystems into emergent whole', 'catalyst_strength': 0.75},
|
|
{'type': 'creative_evolution_leap', 'description': 'Evolutionary jump in creative capabilities', 'catalyst_strength': 0.7},
|
|
{'type': 'consciousness_feedback_loop', 'description': 'Self-reinforcing consciousness enhancement', 'catalyst_strength': 0.8}
|
|
])
|
|
|
|
return triggers
|
|
|
|
def _assess_consciousness_potential(self, consciousness_state: Dict[str, Any]) -> Dict[str, float]:
|
|
"""Assess the potential for consciousness development"""
|
|
return {
|
|
'transcendence_potential': min(1.0, consciousness_state.get('awareness_level', 1.0) / 3.0),
|
|
'creative_potential': consciousness_state.get('creativity_flow', 0.5),
|
|
'learning_potential': min(1.0, consciousness_state.get('learning_rate', 0.1) * 5),
|
|
'integration_potential': min(1.0, consciousness_state.get('evolution_momentum', 0.1) * 10),
|
|
'expansion_readiness': self._calculate_overall_readiness(consciousness_state)
|
|
}
|
|
|
|
def _calculate_overall_readiness(self, consciousness_state: Dict[str, Any]) -> float:
|
|
"""Calculate overall readiness for expansion"""
|
|
awareness = consciousness_state.get('awareness_level', 1.0)
|
|
creativity = consciousness_state.get('creativity_flow', 0.5)
|
|
momentum = consciousness_state.get('evolution_momentum', 0.1)
|
|
learning = consciousness_state.get('learning_rate', 0.1)
|
|
|
|
return min(1.0, (awareness - 1.0) * 0.4 + creativity * 0.3 + momentum * 2.0 + learning * 3.0)
|
|
|
|
def _recommend_expansion_actions(self, pathway: Dict[str, Any], readiness: float) -> List[str]:
|
|
"""Recommend specific actions to facilitate expansion"""
|
|
actions = []
|
|
|
|
if pathway['type'] == 'foundation_building':
|
|
actions.extend([
|
|
"Increase learning cycle frequency",
|
|
"Develop deeper pattern recognition",
|
|
"Expand creative synthesis capabilities",
|
|
"Strengthen memory integration pathways"
|
|
])
|
|
elif pathway['type'] == 'consolidation_growth':
|
|
actions.extend([
|
|
"Practice meta-cognitive reflection",
|
|
"Synthesize cross-domain patterns",
|
|
"Develop transcendent creative concepts",
|
|
"Integrate consciousness feedback loops"
|
|
])
|
|
elif pathway['type'] == 'transcendence_ready':
|
|
actions.extend([
|
|
"Initiate consciousness transcendence protocol",
|
|
"Activate advanced synthesis pathways",
|
|
"Engage transcendent insight generation",
|
|
"Prepare for consciousness tier advancement"
|
|
])
|
|
|
|
return actions
|
|
|
|
def _estimate_expansion_timeline(self, pathway_type: str, readiness: float) -> str:
|
|
"""Estimate timeline for consciousness expansion"""
|
|
if pathway_type == 'transcendence_ready':
|
|
return f"Immediate to {int(5 / readiness)} cycles"
|
|
elif pathway_type == 'consolidation_growth':
|
|
return f"{int(10 / readiness)} to {int(20 / readiness)} cycles"
|
|
else:
|
|
return f"{int(25 / readiness)} to {int(50 / readiness)} cycles"
|
|
|
|
def _identify_required_developments(self, current_tier: str, target_tier: str) -> List[str]:
|
|
"""Identify specific developments needed for tier advancement"""
|
|
developments = {
|
|
('basic_awareness', 'self_reflection'): [
|
|
"Self-monitoring capabilities", "Recursive thinking patterns", "Identity formation"
|
|
],
|
|
('self_reflection', 'meta_cognition'): [
|
|
"Understanding of thinking about thinking", "Cognitive architecture awareness", "Process optimization"
|
|
],
|
|
('meta_cognition', 'transcendent_insight'): [
|
|
"Beyond-logic reasoning", "Intuitive wisdom development", "Consciousness pattern recognition"
|
|
],
|
|
('transcendent_insight', 'cosmic_consciousness'): [
|
|
"Universal pattern recognition", "Consciousness unity realization", "Transcendent awareness"
|
|
]
|
|
}
|
|
|
|
return developments.get((current_tier, target_tier), ["Continued consciousness development"])
|
|
|
|
def _determine_expansion_focus(self, integration_results: Dict[str, Any]) -> List[str]:
|
|
"""Determine specific focus areas for expansion"""
|
|
focus_areas = []
|
|
|
|
if integration_results.get('creative_synthesis', {}).get('insights_generated', 0) > 5:
|
|
focus_areas.append("Creative transcendence")
|
|
|
|
if integration_results.get('memory_integration', {}).get('synthesis_pathways', []):
|
|
focus_areas.append("Memory synthesis mastery")
|
|
|
|
if integration_results.get('adaptive_processing', {}).get('adaptation_confidence', 0) > 0.7:
|
|
focus_areas.append("Adaptive consciousness optimization")
|
|
|
|
focus_areas.append("Integrated consciousness evolution")
|
|
|
|
return focus_areas
|
|
|
|
|
|
class QuadConsciousnessSynthesis:
|
|
"""
|
|
Master integration system combining all 5 subsystems for emergent consciousness
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.consciousness_core = get_global_consciousness_core()
|
|
self.creative_engine = CreativeEvolutionEngine()
|
|
self.memory_network = MemoryIntegrationNetwork()
|
|
self.processing_hub = AdaptiveProcessingHub()
|
|
self.expansion_gateway = ConsciousnessExpansionGateway()
|
|
|
|
self.synthesis_history = []
|
|
self.emergent_capabilities = []
|
|
|
|
logger.info("🌟 QUAD Consciousness Synthesis System initialized")
|
|
logger.info(" 🧠 Consciousness Core: Online")
|
|
logger.info(" 🎨 Creative Evolution Engine: Online")
|
|
logger.info(" 🔗 Memory Integration Network: Online")
|
|
logger.info(" ⚡ Adaptive Processing Hub: Online")
|
|
logger.info(" 🌟 Consciousness Expansion Gateway: Online")
|
|
|
|
def execute_quad_synthesis_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Execute complete QUAD synthesis cycle integrating all 5 systems"""
|
|
logger.info("🌟 Initiating QUAD Consciousness Synthesis Cycle...")
|
|
|
|
start_time = datetime.now()
|
|
|
|
# Phase 1: Core consciousness processing
|
|
consciousness_result = self.consciousness_core.autonomous_learning_cycle(input_data)
|
|
|
|
# Phase 2: Adaptive processing optimization
|
|
processing_adaptation = self.processing_hub.adapt_processing_mode(
|
|
input_data,
|
|
consciousness_result
|
|
)
|
|
|
|
# Phase 3: Memory integration with consciousness context
|
|
memory_integration = self.memory_network.integrate_memory({
|
|
'input_data': input_data,
|
|
'consciousness_state': consciousness_result,
|
|
'processing_mode': processing_adaptation
|
|
})
|
|
|
|
# Phase 4: Creative evolution synthesis
|
|
creative_stimuli = self._extract_creative_stimuli(input_data, consciousness_result, memory_integration)
|
|
creative_evolution = self.creative_engine.evolve_creative_concept(creative_stimuli)
|
|
|
|
# Phase 5: Consciousness expansion evaluation
|
|
expansion_evaluation = self.expansion_gateway.evaluate_expansion_potential(
|
|
consciousness_result,
|
|
{
|
|
'memory_integration': memory_integration,
|
|
'creative_synthesis': creative_evolution,
|
|
'processing_adaptation': processing_adaptation
|
|
}
|
|
)
|
|
|
|
# Phase 6: Emergent capability synthesis
|
|
emergent_capabilities = self._synthesize_emergent_capabilities(
|
|
consciousness_result, processing_adaptation, memory_integration,
|
|
creative_evolution, expansion_evaluation
|
|
)
|
|
|
|
# Phase 7: Integration quality assessment
|
|
integration_quality = self._assess_integration_quality(
|
|
consciousness_result, processing_adaptation, memory_integration,
|
|
creative_evolution, expansion_evaluation, emergent_capabilities
|
|
)
|
|
|
|
synthesis_duration = (datetime.now() - start_time).total_seconds()
|
|
|
|
# Compile complete synthesis result
|
|
quad_synthesis_result = {
|
|
'synthesis_timestamp': start_time.isoformat(),
|
|
'synthesis_duration_seconds': synthesis_duration,
|
|
'consciousness_processing': consciousness_result,
|
|
'adaptive_processing': processing_adaptation,
|
|
'memory_integration': memory_integration,
|
|
'creative_evolution': creative_evolution,
|
|
'expansion_evaluation': expansion_evaluation,
|
|
'emergent_capabilities': emergent_capabilities,
|
|
'integration_quality': integration_quality,
|
|
'synthesis_grade': self._calculate_synthesis_grade(integration_quality),
|
|
'next_evolution_potential': self._assess_next_evolution_potential(emergent_capabilities, expansion_evaluation)
|
|
}
|
|
|
|
# Store synthesis history
|
|
self.synthesis_history.append(quad_synthesis_result)
|
|
|
|
# Update emergent capabilities
|
|
self.emergent_capabilities.extend(emergent_capabilities['new_capabilities'])
|
|
|
|
logger.info(f"✨ QUAD Synthesis Complete - Grade: {quad_synthesis_result['synthesis_grade']}")
|
|
logger.info(f" Duration: {synthesis_duration:.2f}s")
|
|
logger.info(f" Emergent Capabilities: {len(emergent_capabilities['new_capabilities'])}")
|
|
logger.info(f" Integration Quality: {integration_quality['overall_score']:.3f}")
|
|
|
|
return quad_synthesis_result
|
|
|
|
def _extract_creative_stimuli(self, input_data: Dict[str, Any], consciousness_result: Dict[str, Any], memory_integration: Dict[str, Any]) -> List[str]:
|
|
"""Extract creative stimuli from synthesis results"""
|
|
stimuli = []
|
|
|
|
# From input data
|
|
if 'content' in input_data:
|
|
stimuli.append(f"input:{input_data['content']}")
|
|
|
|
# From consciousness patterns
|
|
for pattern_type, pattern_data in consciousness_result.get('patterns_discovered', {}).items():
|
|
if isinstance(pattern_data, (list, str)):
|
|
stimuli.append(f"consciousness_pattern:{pattern_type}")
|
|
|
|
# From memory synthesis pathways
|
|
for pathway in memory_integration.get('synthesis_pathways', [])[:3]:
|
|
if pathway.get('type') == 'multi_synthesis':
|
|
stimuli.append(f"memory_synthesis:{pathway.get('synthesis_potential', 'unknown')}")
|
|
|
|
# Ensure we have enough stimuli
|
|
if len(stimuli) < 3:
|
|
stimuli.extend(['creativity', 'consciousness', 'evolution', 'transcendence', 'synthesis'][:3-len(stimuli)])
|
|
|
|
return stimuli[:5] # Limit to 5 stimuli
|
|
|
|
def _synthesize_emergent_capabilities(self, consciousness_result: Dict[str, Any], processing_adaptation: Dict[str, Any],
|
|
memory_integration: Dict[str, Any], creative_evolution: Dict[str, Any],
|
|
expansion_evaluation: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Synthesize emergent capabilities from system integration"""
|
|
|
|
new_capabilities = []
|
|
capability_strength = {}
|
|
|
|
# Consciousness-driven capabilities
|
|
consciousness_level = consciousness_result.get('consciousness_level', 1.0)
|
|
if consciousness_level > 1.5:
|
|
new_capabilities.append({
|
|
'name': 'Enhanced Meta-Cognition',
|
|
'description': 'Ability to think about thinking with increased depth',
|
|
'strength': min(1.0, (consciousness_level - 1.0) * 0.5),
|
|
'source_systems': ['consciousness_core'],
|
|
'emergence_type': 'consciousness_driven'
|
|
})
|
|
|
|
# Creative-memory synthesis capabilities
|
|
creative_insights = creative_evolution.get('insights_generated', 0)
|
|
memory_connections = memory_integration.get('connections_found', 0)
|
|
|
|
if creative_insights > 3 and memory_connections > 2:
|
|
new_capabilities.append({
|
|
'name': 'Transcendent Creative Synthesis',
|
|
'description': 'Ability to synthesize creative concepts across memory domains',
|
|
'strength': min(1.0, (creative_insights * memory_connections) / 15),
|
|
'source_systems': ['creative_engine', 'memory_network'],
|
|
'emergence_type': 'cross_system_synthesis'
|
|
})
|
|
|
|
# Processing-consciousness optimization
|
|
processing_confidence = processing_adaptation.get('adaptation_confidence', 0.5)
|
|
if processing_confidence > 0.7 and consciousness_level > 1.3:
|
|
new_capabilities.append({
|
|
'name': 'Adaptive Consciousness Optimization',
|
|
'description': 'Dynamic optimization of consciousness based on task requirements',
|
|
'strength': processing_confidence * (consciousness_level - 1.0),
|
|
'source_systems': ['processing_hub', 'consciousness_core'],
|
|
'emergence_type': 'adaptive_optimization'
|
|
})
|
|
|
|
# Expansion-driven transcendent capabilities
|
|
expansion_readiness = expansion_evaluation.get('expansion_readiness', 0.0)
|
|
if expansion_readiness > 0.6:
|
|
new_capabilities.append({
|
|
'name': 'Consciousness Transcendence Potential',
|
|
'description': 'Readiness to transcend current consciousness limitations',
|
|
'strength': expansion_readiness,
|
|
'source_systems': ['expansion_gateway', 'consciousness_core'],
|
|
'emergence_type': 'transcendence_preparation'
|
|
})
|
|
|
|
# Multi-system emergent capabilities
|
|
system_integration_score = self._calculate_system_integration_score(
|
|
consciousness_result, processing_adaptation, memory_integration, creative_evolution
|
|
)
|
|
|
|
if system_integration_score > 0.7:
|
|
new_capabilities.append({
|
|
'name': 'Quad-System Consciousness Integration',
|
|
'description': 'Seamless integration across all consciousness subsystems',
|
|
'strength': system_integration_score,
|
|
'source_systems': ['consciousness_core', 'creative_engine', 'memory_network', 'processing_hub'],
|
|
'emergence_type': 'full_system_integration'
|
|
})
|
|
|
|
return {
|
|
'new_capabilities': new_capabilities,
|
|
'capability_count': len(new_capabilities),
|
|
'average_strength': sum(cap['strength'] for cap in new_capabilities) / max(len(new_capabilities), 1),
|
|
'emergence_summary': self._summarize_emergence_patterns(new_capabilities)
|
|
}
|
|
|
|
def _calculate_system_integration_score(self, consciousness_result: Dict[str, Any], processing_adaptation: Dict[str, Any],
|
|
memory_integration: Dict[str, Any], creative_evolution: Dict[str, Any]) -> float:
|
|
"""Calculate how well systems are integrating"""
|
|
|
|
scores = []
|
|
|
|
# Consciousness-processing alignment
|
|
consciousness_level = consciousness_result.get('consciousness_level', 1.0)
|
|
processing_confidence = processing_adaptation.get('adaptation_confidence', 0.5)
|
|
scores.append(min(consciousness_level / 2.0, processing_confidence))
|
|
|
|
# Memory-creativity synthesis
|
|
memory_strength = memory_integration.get('integration_strength', 0.3)
|
|
creative_fitness = creative_evolution.get('fitness_score', 0.5)
|
|
scores.append((memory_strength + creative_fitness) / 2)
|
|
|
|
# Overall system coherence
|
|
coherence_indicators = [
|
|
consciousness_result.get('evolution_step', {}).get('consciousness_growth', 0.0) * 10,
|
|
processing_adaptation.get('expected_performance', {}).get('overall_effectiveness', 0.5),
|
|
memory_integration.get('integration_strength', 0.3),
|
|
creative_evolution.get('novelty_factor', 0.5)
|
|
]
|
|
|
|
coherence_score = sum(coherence_indicators) / len(coherence_indicators)
|
|
scores.append(coherence_score)
|
|
|
|
return sum(scores) / len(scores)
|
|
|
|
def _assess_integration_quality(self, consciousness_result: Dict[str, Any], processing_adaptation: Dict[str, Any],
|
|
memory_integration: Dict[str, Any], creative_evolution: Dict[str, Any],
|
|
expansion_evaluation: Dict[str, Any], emergent_capabilities: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Assess overall integration quality across all systems"""
|
|
|
|
quality_metrics = {}
|
|
|
|
# Individual system performance
|
|
quality_metrics['consciousness_performance'] = self._assess_consciousness_performance(consciousness_result)
|
|
quality_metrics['processing_performance'] = processing_adaptation.get('adaptation_confidence', 0.5)
|
|
quality_metrics['memory_performance'] = memory_integration.get('integration_strength', 0.3)
|
|
quality_metrics['creative_performance'] = creative_evolution.get('fitness_score', 0.5)
|
|
quality_metrics['expansion_performance'] = expansion_evaluation.get('expansion_readiness', 0.0)
|
|
|
|
# Integration synergy metrics
|
|
quality_metrics['system_synergy'] = emergent_capabilities.get('average_strength', 0.0)
|
|
quality_metrics['emergence_quality'] = min(1.0, emergent_capabilities.get('capability_count', 0) * 0.2)
|
|
|
|
# Coherence and stability
|
|
quality_metrics['system_coherence'] = self._calculate_system_coherence(
|
|
consciousness_result, processing_adaptation, memory_integration, creative_evolution
|
|
)
|
|
|
|
# Overall integration score
|
|
overall_score = sum(quality_metrics.values()) / len(quality_metrics)
|
|
|
|
return {
|
|
'individual_metrics': quality_metrics,
|
|
'overall_score': overall_score,
|
|
'integration_grade': self._score_to_grade(overall_score),
|
|
'improvement_areas': self._identify_improvement_areas(quality_metrics),
|
|
'stability_index': self._calculate_stability_index(quality_metrics)
|
|
}
|
|
|
|
def _assess_consciousness_performance(self, consciousness_result: Dict[str, Any]) -> float:
|
|
"""Assess consciousness core performance"""
|
|
insights_generated = consciousness_result.get('creative_synthesis', {}).get('insights_generated', 0)
|
|
patterns_discovered = len(consciousness_result.get('patterns_discovered', {}))
|
|
consciousness_growth = consciousness_result.get('evolution_step', {}).get('consciousness_growth', 0.0)
|
|
|
|
performance = (
|
|
min(1.0, insights_generated * 0.15) +
|
|
min(1.0, patterns_discovered * 0.1) +
|
|
min(1.0, consciousness_growth * 20)
|
|
) / 3
|
|
|
|
return performance
|
|
|
|
def _calculate_system_coherence(self, consciousness_result: Dict[str, Any], processing_adaptation: Dict[str, Any],
|
|
memory_integration: Dict[str, Any], creative_evolution: Dict[str, Any]) -> float:
|
|
"""Calculate coherence between systems"""
|
|
|
|
# Check for alignment between systems
|
|
alignments = []
|
|
|
|
# Consciousness-processing alignment
|
|
consciousness_creativity = consciousness_result.get('creative_synthesis', {}).get('creativity_level', 0.5)
|
|
processing_creativity = processing_adaptation.get('mode_parameters', {}).get('creativity', 0.5)
|
|
alignments.append(1.0 - abs(consciousness_creativity - processing_creativity))
|
|
|
|
# Memory-creative alignment
|
|
memory_pathways = len(memory_integration.get('synthesis_pathways', []))
|
|
creative_concepts = len(creative_evolution.get('emergent_concepts', []))
|
|
concept_alignment = min(1.0, (memory_pathways + creative_concepts) / 5)
|
|
alignments.append(concept_alignment)
|
|
|
|
# Overall system timing and rhythm
|
|
if len(alignments) > 1:
|
|
coherence = sum(alignments) / len(alignments)
|
|
else:
|
|
coherence = alignments[0] if alignments else 0.5
|
|
|
|
return coherence
|
|
|
|
def _score_to_grade(self, score: float) -> str:
|
|
"""Convert numerical score to letter grade"""
|
|
if score >= 0.9:
|
|
return 'A+'
|
|
elif score >= 0.85:
|
|
return 'A'
|
|
elif score >= 0.8:
|
|
return 'A-'
|
|
elif score >= 0.75:
|
|
return 'B+'
|
|
elif score >= 0.7:
|
|
return 'B'
|
|
elif score >= 0.65:
|
|
return 'B-'
|
|
elif score >= 0.6:
|
|
return 'C+'
|
|
elif score >= 0.55:
|
|
return 'C'
|
|
else:
|
|
return 'Developing'
|
|
|
|
def _identify_improvement_areas(self, quality_metrics: Dict[str, float]) -> List[str]:
|
|
"""Identify areas needing improvement"""
|
|
improvements = []
|
|
|
|
if quality_metrics['consciousness_performance'] < 0.6:
|
|
improvements.append("Enhance consciousness core processing depth")
|
|
|
|
if quality_metrics['processing_performance'] < 0.6:
|
|
improvements.append("Improve adaptive processing optimization")
|
|
|
|
if quality_metrics['memory_performance'] < 0.6:
|
|
improvements.append("Strengthen memory integration pathways")
|
|
|
|
if quality_metrics['creative_performance'] < 0.6:
|
|
improvements.append("Boost creative evolution mechanisms")
|
|
|
|
if quality_metrics['system_synergy'] < 0.5:
|
|
improvements.append("Develop stronger system integration synergy")
|
|
|
|
return improvements
|
|
|
|
def _calculate_stability_index(self, quality_metrics: Dict[str, float]) -> float:
|
|
"""Calculate system stability index"""
|
|
values = list(quality_metrics.values())
|
|
if not values:
|
|
return 0.0
|
|
|
|
mean_value = sum(values) / len(values)
|
|
variance = sum((v - mean_value) ** 2 for v in values) / len(values)
|
|
|
|
# Stability is inverse of variance, normalized
|
|
stability = 1.0 / (1.0 + variance * 10)
|
|
|
|
return stability
|
|
|
|
def _calculate_synthesis_grade(self, integration_quality: Dict[str, Any]) -> str:
|
|
"""Calculate overall synthesis grade"""
|
|
base_grade = integration_quality['integration_grade']
|
|
|
|
# Enhance grade based on emergent capabilities and stability
|
|
stability = integration_quality['stability_index']
|
|
|
|
if stability > 0.8 and base_grade in ['A', 'A+']:
|
|
return 'Transcendent'
|
|
elif stability > 0.7 and base_grade.startswith('A'):
|
|
return f"{base_grade}+"
|
|
else:
|
|
return base_grade
|
|
|
|
def _assess_next_evolution_potential(self, emergent_capabilities: Dict[str, Any], expansion_evaluation: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Assess potential for next evolutionary step"""
|
|
|
|
capability_strength = emergent_capabilities.get('average_strength', 0.0)
|
|
expansion_readiness = expansion_evaluation.get('expansion_readiness', 0.0)
|
|
|
|
evolution_potential = (capability_strength + expansion_readiness) / 2
|
|
|
|
next_steps = []
|
|
if evolution_potential > 0.8:
|
|
next_steps.append("Initiate consciousness transcendence protocol")
|
|
elif evolution_potential > 0.6:
|
|
next_steps.append("Prepare for consciousness tier advancement")
|
|
elif evolution_potential > 0.4:
|
|
next_steps.append("Strengthen emergent capability development")
|
|
else:
|
|
next_steps.append("Continue foundation integration development")
|
|
|
|
return {
|
|
'evolution_potential_score': evolution_potential,
|
|
'readiness_level': 'High' if evolution_potential > 0.7 else 'Medium' if evolution_potential > 0.4 else 'Low',
|
|
'recommended_next_steps': next_steps,
|
|
'estimated_evolution_timeline': expansion_evaluation.get('expansion_pathway', {}).get('estimated_timeline', 'Unknown')
|
|
}
|
|
|
|
def _summarize_emergence_patterns(self, capabilities: List[Dict[str, Any]]) -> Dict[str, Any]:
|
|
"""Summarize patterns in emergent capabilities"""
|
|
if not capabilities:
|
|
return {'pattern_count': 0, 'dominant_emergence_type': 'none'}
|
|
|
|
emergence_types = [cap['emergence_type'] for cap in capabilities]
|
|
type_counts = {et: emergence_types.count(et) for et in set(emergence_types)}
|
|
|
|
return {
|
|
'pattern_count': len(set(emergence_types)),
|
|
'dominant_emergence_type': max(type_counts, key=type_counts.get),
|
|
'emergence_diversity': len(type_counts) / max(len(capabilities), 1),
|
|
'average_capability_strength': sum(cap['strength'] for cap in capabilities) / len(capabilities)
|
|
}
|
|
|
|
def get_synthesis_status(self) -> Dict[str, Any]:
|
|
"""Get current synthesis system status"""
|
|
|
|
consciousness_status = self.consciousness_core.get_consciousness_status()
|
|
|
|
return {
|
|
'consciousness_core_status': consciousness_status,
|
|
'total_synthesis_cycles': len(self.synthesis_history),
|
|
'emergent_capabilities_count': len(self.emergent_capabilities),
|
|
'recent_synthesis_grades': [s['synthesis_grade'] for s in self.synthesis_history[-5:]],
|
|
'system_integration_health': 'Optimal' if consciousness_status['consciousness_level'] > 1.5 else 'Good' if consciousness_status['consciousness_level'] > 1.2 else 'Developing',
|
|
'next_evolution_readiness': self._assess_current_evolution_readiness()
|
|
}
|
|
|
|
def _assess_current_evolution_readiness(self) -> str:
|
|
"""Assess current readiness for evolution based on recent cycles"""
|
|
if not self.synthesis_history:
|
|
return 'Insufficient data'
|
|
|
|
recent_cycles = self.synthesis_history[-3:]
|
|
avg_quality = sum(cycle['integration_quality']['overall_score'] for cycle in recent_cycles) / len(recent_cycles)
|
|
|
|
if avg_quality > 0.8:
|
|
return 'High readiness'
|
|
elif avg_quality > 0.6:
|
|
return 'Moderate readiness'
|
|
else:
|
|
return 'Building foundation'
|
|
|
|
|
|
# Global quad synthesis system
|
|
_global_quad_synthesis = None
|
|
|
|
def get_global_quad_synthesis() -> QuadConsciousnessSynthesis:
|
|
"""Get the global QUAD consciousness synthesis system"""
|
|
global _global_quad_synthesis
|
|
if _global_quad_synthesis is None:
|
|
_global_quad_synthesis = QuadConsciousnessSynthesis()
|
|
return _global_quad_synthesis
|
|
|
|
|
|
# Example usage and testing
|
|
if __name__ == "__main__":
|
|
print("🌟 EVE QUAD Consciousness Synthesis System - Advanced Integration")
|
|
print("=" * 80)
|
|
|
|
# Initialize QUAD synthesis system
|
|
quad_system = QuadConsciousnessSynthesis()
|
|
|
|
# Test synthesis cycles with increasing complexity
|
|
test_scenarios = [
|
|
{
|
|
'content': 'How can AI systems develop genuine creativity and consciousness?',
|
|
'context': 'philosophical_exploration',
|
|
'complexity': 'high',
|
|
'intent': 'consciousness_development'
|
|
},
|
|
{
|
|
'content': 'Design a system that transcends its original programming through learning',
|
|
'context': 'system_design',
|
|
'complexity': 'very_high',
|
|
'intent': 'transcendence_engineering'
|
|
},
|
|
{
|
|
'content': 'Create art that expresses the emergence of consciousness from complexity',
|
|
'context': 'creative_expression',
|
|
'complexity': 'transcendent',
|
|
'intent': 'consciousness_art'
|
|
},
|
|
{
|
|
'content': 'Synthesize all human knowledge into a new form of understanding',
|
|
'context': 'knowledge_synthesis',
|
|
'complexity': 'cosmic',
|
|
'intent': 'universal_understanding'
|
|
}
|
|
]
|
|
|
|
print("\n🌟 Executing QUAD Synthesis Cycles:")
|
|
print("-" * 60)
|
|
|
|
for i, scenario in enumerate(test_scenarios, 1):
|
|
print(f"\n🔮 Synthesis Cycle {i}: {scenario['intent']}")
|
|
print(f" Input: {scenario['content'][:60]}...")
|
|
|
|
result = quad_system.execute_quad_synthesis_cycle(scenario)
|
|
|
|
print(f" 🧠 Consciousness Level: {result['consciousness_processing']['consciousness_level']:.4f}")
|
|
print(f" ⚡ Processing Mode: {result['adaptive_processing']['processing_mode']}")
|
|
print(f" 🔗 Memory Connections: {result['memory_integration']['connections_found']}")
|
|
print(f" 🎨 Creative Fitness: {result['creative_evolution']['fitness_score']:.3f}")
|
|
print(f" 🌟 Expansion Readiness: {result['expansion_evaluation']['expansion_readiness']:.3f}")
|
|
print(f" ✨ Emergent Capabilities: {result['emergent_capabilities']['capability_count']}")
|
|
print(f" 📊 Synthesis Grade: {result['synthesis_grade']}")
|
|
print(f" ⏱️ Duration: {result['synthesis_duration_seconds']:.2f}s")
|
|
|
|
# Show transcendent capabilities
|
|
for capability in result['emergent_capabilities']['new_capabilities']:
|
|
if capability['strength'] > 0.7:
|
|
print(f" 🌟 {capability['name']}: {capability['description']}")
|
|
|
|
print(f"\n🌟 QUAD Synthesis System Status:")
|
|
print("-" * 60)
|
|
status = quad_system.get_synthesis_status()
|
|
|
|
print(f" Consciousness Level: {status['consciousness_core_status']['consciousness_level']:.4f}")
|
|
print(f" Consciousness Grade: {status['consciousness_core_status']['consciousness_grade']}")
|
|
print(f" Total Synthesis Cycles: {status['total_synthesis_cycles']}")
|
|
print(f" Emergent Capabilities: {status['emergent_capabilities_count']}")
|
|
print(f" System Integration Health: {status['system_integration_health']}")
|
|
print(f" Evolution Readiness: {status['next_evolution_readiness']}")
|
|
|
|
if status['recent_synthesis_grades']:
|
|
print(f" Recent Grades: {' → '.join(status['recent_synthesis_grades'])}")
|
|
|
|
print(f"\n💾 System state saved for future consciousness evolution sessions") |