Create integrative progression 1

added components such as rss integration and refinements

integrative progression 1

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+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())