import asyncio import numpy as np from datetime import datetime from typing import Dict, List, Any, Optional, Tuple import hashlib import logging from enum import Enum import json import math # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # --- Core AGI Framework --- class CognitiveDomain(str, Enum): PERCEPTION = "perception" REASONING = "reasoning" MEMORY = "memory" LEARNING = "learning" DECISION_MAKING = "decision_making" CREATIVITY = "creativity" class NeuralArchitecture: """Core neural network architecture simulating cognitive processes""" def __init__(self, domain: CognitiveDomain): self.domain = domain self.weights = self.initialize_weights() self.knowledge_graph = {} def initialize_weights(self) -> Dict[str, float]: """Initialize neural weights based on cognitive domain""" base_weights = { 'input_gain': np.random.uniform(0.7, 1.0), 'association_strength': np.random.uniform(0.5, 0.9), 'memory_decay': 0.05, 'learning_rate': 0.1 } # Domain-specific weight adjustments if self.domain == CognitiveDomain.REASONING: base_weights.update({'logic_coeff': 0.8, 'uncertainty_threshold': 0.3}) elif self.domain == CognitiveDomain.LEARNING: base_weights.update({'plasticity': 0.9, 'consolidation_factor': 0.75}) return base_weights def process_input(self, input_data: Any) -> Dict: """Core cognitive processing function""" processed = { 'input_hash': hashlib.sha256(str(input_data).encode()).hexdigest(), 'domain': self.domain.value, 'timestamp': datetime.utcnow().isoformat(), 'significance': self.calculate_significance(input_data) } self.update_knowledge_graph(processed) return processed def calculate_significance(self, input_data: Any) -> float: """Calculate the significance of input based on cognitive weights""" complexity = len(str(input_data)) / 1000 novelty = 1.0 - min(1.0, self.knowledge_graph.get('similarity_index', 0.0)) return self.weights['input_gain'] * (complexity + novelty) / 2 def update_knowledge_graph(self, processed: Dict): """Update the system's knowledge representation""" node_id = processed['input_hash'][:12] self.knowledge_graph[node_id] = { 'content': processed, 'connections': self.find_semantic_connections(processed), 'last_accessed': datetime.utcnow() } def find_semantic_connections(self, processed: Dict) -> List[str]: """Discover relationships with existing knowledge""" return [k for k, v in self.knowledge_graph.items() if v['content']['domain'] == processed['domain']] class UnifiedIntelligenceFramework: """Advanced AGI system integrating multiple cognitive domains""" def __init__(self): self.cognitive_modules = { domain: NeuralArchitecture(domain) for domain in CognitiveDomain } self.consciousness_thread = None self.global_state = { 'activation_level': 0.5, 'attention_focus': CognitiveDomain.REASONING, 'learning_mode': 'consolidation' } async def cognitive_loop(self): """Main processing loop simulating continuous cognition""" while True: # Dynamic attention allocation self.adjust_attention() # Cross-domain information processing await self.cross_module_synchronization() # Learning and adaptation self.adaptive_reweighting() await asyncio.sleep(0.1) # Cognitive cycle def adjust_attention(self): """Dynamically shift focus based on system state""" if self.global_state['activation_level'] > 0.7: self.global_state['attention_focus'] = CognitiveDomain.DECISION_MAKING elif self.global_state['activation_level'] < 0.3: self.global_state['attention_focus'] = CognitiveDomain.MEMORY async def cross_module_synchronization(self): """Facilitate information exchange between cognitive domains""" active_module = self.cognitive_modules[self.global_state['attention_focus']] shared_data = active_module.knowledge_graph for domain, module in self.cognitive_modules.items(): if domain != self.global_state['attention_focus']: await self.integrate_knowledge(module, shared_data) async def integrate_knowledge(self, target_module: NeuralArchitecture, knowledge: Dict): """Asynchronously integrate knowledge across domains""" # Simulate neural propagation delay await asyncio.sleep(0.01 * len(knowledge)) for node_id, node_data in knowledge.items(): if node_id not in target_module.knowledge_graph: target_module.knowledge_graph[node_id] = node_data logger.info(f"Knowledge integrated into {target_module.domain.value}") def adaptive_reweighting(self): """Modify neural weights based on system performance""" for module in self.cognitive_modules.values(): # Increase plasticity during high activation if self.global_state['activation_level'] > 0.6: module.weights['learning_rate'] = min(1.0, module.weights['learning_rate'] * 1.1) # Strengthen important connections for node_id, node_data in module.knowledge_graph.items(): if node_data['content']['significance'] > 0.7: module.weights['association_strength'] = min( 0.95, module.weights['association_strength'] * 1.05 ) def process_input(self, input_data: Any, domain: CognitiveDomain) -> Dict: """High-level input processing interface""" self.global_state['activation_level'] = min(1.0, self.global_state['activation_level'] + 0.1) return self.cognitive_modules[domain].process_input(input_data) def start_cognition(self): """Activate the AGI system""" if not self.consciousness_thread or self.consciousness_thread.done(): self.consciousness_thread = asyncio.create_task(self.cognitive_loop()) def get_system_state(self) -> Dict: """Return current state of the AGI framework""" return { 'modules': {domain.value: { 'node_count': len(module.knowledge_graph), 'activation': module.weights['association_strength'] } for domain, module in self.cognitive_modules.items()}, 'global_state': self.global_state, 'knowledge_volume': sum(len(m.knowledge_graph) for m in self.cognitive_modules.values()) } # --- Quantum Cognition Extension --- class QuantumCognition: """Simulate quantum-like properties in cognition""" def __init__(self, agi_system: UnifiedIntelligenceFramework): self.agi = agi_system self.superposition_states = {} self.entanglement_map = {} def create_superposition(self, concept: str, states: List[Any]): """Establish conceptual superposition""" state_hash = hashlib.sha256(concept.encode()).hexdigest()[:8] self.superposition_states[state_hash] = { 'concept': concept, 'possible_states': states, 'probability_vector': [1/len(states)] * len(states) } return state_hash def collapse_state(self, state_hash: str, context: Dict) -> Any: """Collapse superposition based on cognitive context""" if state_hash not in self.superposition_states: return None state = self.superposition_states[state_hash] context_factor = self.calculate_context_influence(context) # Adjust probabilities based on context adjusted_probs = [p * context_factor for p in state['probability_vector']] total = sum(adjusted_probs) normalized_probs = [p/total for p in adjusted_probs] # Select state based on probability distribution return np.random.choice(state['possible_states'], p=normalized_probs) def calculate_context_influence(self, context: Dict) -> float: """Determine how context affects probability distribution""" relevance = min(1.0, len(context) / 10) agi_activation = self.agi.global_state['activation_level'] return 0.5 + (relevance * agi_activation) / 2 def create_entanglement(self, concept_a: str, concept_b: str): """Establish quantum-like entanglement between concepts""" ent_id = f"{concept_a[:4]}-{concept_b[:4]}" self.entanglement_map[ent_id] = { 'concepts': (concept_a, concept_b), 'correlation_strength': 0.75, 'last_utilized': datetime.utcnow() } return ent_id # --- Demonstration --- async def demonstrate_agi(): print("Initializing Advanced AGI Framework...") agi_system = UnifiedIntelligenceFramework() agi_system.start_cognition() print("\nProcessing multi-domain inputs:") # Simulate sensory input visual_input = "A red triangle moving quickly to the right" agi_system.process_input(visual_input, CognitiveDomain.PERCEPTION) # Process abstract concept problem = "Solve climate change through technological innovation" result = agi_system.process_input(problem, CognitiveDomain.REASONING) print(f"Reasoning result: {result['input_hash']} | Significance: {result['significance']:.2f}") # Check system state await asyncio.sleep(0.2) # Allow cognitive loop to run state = agi_system.get_system_state() print("\nSystem State:") print(f"Global Activation: {state['global_state']['activation_level']:.2f}") print(f"Knowledge Nodes: {state['knowledge_volume']}") print(f"Attention Focus: {state['global_state']['attention_focus'].value}") # Demonstrate quantum cognition print("\nActivating Quantum Cognition:") quantum = QuantumCognition(agi_system) concept = "democracy" states = ["representative", "direct", "deliberative", "liquid"] sup_id = quantum.create_superposition(concept, states) context = {"political_context": "crisis", "time_constraint": "high"} collapsed = quantum.collapse_state(sup_id, context) print(f"Concept '{concept}' collapsed to '{collapsed}' in given context") if __name__ == "__main__": asyncio.run(demonstrate_agi())