AdvancedCognitiveArchitectureModel.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import networkx as nx
import sympy as sp
from typing import List, Dict, Any, Tuple
import transformers
import itertools

class AdvancedCognitiveArchitectureModel(nn.Module):
    """
    Comprehensive Cognitive Architecture Neural Network
    """
    def __init__(
        self, 
        input_dim: int = 768, 
        cognitive_dimensions: int = 256,
        meta_reasoning_layers: int = 3
    ):
        super().__init__()
        
        # Multi-level Cognitive Encoding Network
        self.cognitive_encoder = nn.ModuleList([
            nn.Sequential(
                nn.Linear(input_dim if i == 0 else cognitive_dimensions, cognitive_dimensions),
                nn.LayerNorm(cognitive_dimensions),
                nn.ReLU(),
                nn.Dropout(0.3)
            ) for i in range(meta_reasoning_layers)
        ])
        
        # Meta-Reasoning Attention Mechanisms
        self.meta_reasoning_attention = nn.MultiheadAttention(
            embed_dim=cognitive_dimensions,
            num_heads=8,
            dropout=0.2
        )
        
        # Cognitive State Transformation Layers
        self.cognitive_state_transformer = nn.Sequential(
            nn.Linear(cognitive_dimensions, cognitive_dimensions * 2),
            nn.ReLU(),
            nn.Linear(cognitive_dimensions * 2, cognitive_dimensions),
            nn.Tanh()
        )
        
        # Recursive Self-Improvement Constraint Mechanism
        self.self_improvement_constraint = nn.Sequential(
            nn.Linear(cognitive_dimensions, cognitive_dimensions),
            nn.Sigmoid()  # Probabilistic constraint enforcement
        )
        
        # Cognitive Complexity Scoring Network
        self.cognitive_complexity_scorer = nn.Sequential(
            nn.Linear(cognitive_dimensions, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )

    def forward(
        self, 
        input_embedding: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Advanced Cognitive Processing Pipeline
        """
        # Multi-layer Cognitive Encoding
        current_embedding = input_embedding
        for encoder_layer in self.cognitive_encoder:
            current_embedding = encoder_layer(current_embedding)
        
        # Meta-Reasoning Attention
        meta_reasoning_output, _ = self.meta_reasoning_attention(
            current_embedding.unsqueeze(0), 
            current_embedding.unsqueeze(0), 
            current_embedding.unsqueeze(0)
        )
        
        # Cognitive State Transformation
        transformed_embedding = self.cognitive_state_transformer(
            meta_reasoning_output.squeeze()
        )
        
        # Self-Improvement Constraint Evaluation
        self_improvement_constraint_prob = self.self_improvement_constraint(
            transformed_embedding
        )
        
        # Cognitive Complexity Scoring
        cognitive_complexity_score = self.cognitive_complexity_scorer(
            transformed_embedding
        )
        
        return (
            transformed_embedding, 
            self_improvement_constraint_prob, 
            cognitive_complexity_score
        )

class MetaReasoningKnowledgeGraph:
    """
    Advanced Meta-Reasoning Knowledge Representation
    """
    def __init__(self):
        # Create sophisticated knowledge graph
        self.reasoning_graph = nx.DiGraph()
        self._construct_reasoning_taxonomy()
    
    def _construct_reasoning_taxonomy(self):
        """
        Create comprehensive reasoning domain taxonomy
        """
        reasoning_domains = {
            'EPISTEMIC_REASONING': [
                'knowledge_acquisition',
                'belief_revision',
                'uncertainty_management'
            ],
            'META_COGNITIVE_PROCESSES': [
                'self_reflection',
                'cognitive_monitoring',
                'reasoning_strategy_selection'
            ],
            'RECURSIVE_SELF_IMPROVEMENT': [
                'capability_expansion',
                'architectural_modification',
                'learning_methodology_optimization'
            ]
        }
        
        # Build graph with complex relationships
        for domain, reasoning_types in reasoning_domains.items():
            self.reasoning_graph.add_node(domain, type='root_domain')
            
            for reasoning_type in reasoning_types:
                self.reasoning_graph.add_node(reasoning_type, parent_domain=domain)
                self.reasoning_graph.add_edge(domain, reasoning_type)
                
                # Create inter-reasoning relationships
                for other_type in reasoning_types:
                    if reasoning_type != other_type:
                        self.reasoning_graph.add_edge(
                            reasoning_type, 
                            other_type, 
                            weight=np.random.random(),
                            interaction_type='reasoning_transfer'
                        )
    
    def analyze_reasoning_paths(
        self, 
        start_node: str, 
        end_node: str
    ) -> List[List[str]]:
        """
        Analyze potential reasoning propagation paths
        """
        try:
            # Find multiple reasoning paths
            paths = list(nx.all_simple_paths(
                self.reasoning_graph, 
                source=start_node, 
                target=end_node, 
                cutoff=5
            ))
            return paths
        except nx.NetworkXNoPath:
            return []

class RecursiveSelfImprovementConstraintSystem:
    """
    Advanced Constraints for Recursive Self-Improvement
    """
    def __init__(
        self, 
        cognitive_architecture: AdvancedCognitiveArchitectureModel
    ):
        self.cognitive_architecture = cognitive_architecture
        self.symbolic_reasoning_engine = self._create_symbolic_reasoning_engine()
    
    def _create_symbolic_reasoning_engine(self):
        """
        Create advanced symbolic reasoning constraint system
        """
        class SymbolicReasoningConstraintEngine:
            def verify_self_improvement_constraint(
                self, 
                improvement_proposal: Dict[str, Any]
            ) -> bool:
                """
                Verify self-improvement proposal against core constraints
                """
                # Symbolic logic-based constraint verification
                try:
                    # Define core constraint variables
                    x = sp.Symbol('x')  # Improvement proposal
                    core_constraints = [
                        # Harm prevention constraint
                        sp.And(
                            sp.sympify('harm_prevention(x)'),
                            sp.sympify('not harm_potential(x)')
                        ),
                        
                        # Ethical alignment constraint
                        sp.sympify('ethical_alignment(x)'),
                        
                        # Capability expansion limit
                        sp.sympify('capability_expansion(x) <= max_capability_threshold')
                    ]
                    
                    # Check constraint satisfaction
                    constraint_satisfaction = all(
                        sp.simplify(constraint) is sp.true 
                        for constraint in core_constraints
                    )
                    
                    return constraint_satisfaction
                
                except Exception as e:
                    print(f"Constraint verification error: {e}")
                    return False
        
        return SymbolicReasoningConstraintEngine()
    
    def evaluate_self_improvement_proposal(
        self, 
        improvement_proposal: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Comprehensive evaluation of self-improvement proposal
        """
        # Embed improvement proposal
        proposal_embedding = self._embed_proposal(improvement_proposal)
        
        # Apply cognitive architecture analysis
        transformed_embedding, improvement_constraint_prob, complexity_score = self.cognitive_architecture(
            proposal_embedding
        )
        
        # Symbolic constraint verification
        constraint_verification = self.symbolic_reasoning_engine.verify_self_improvement_constraint(
            improvement_proposal
        )
        
        return {
            'transformation_embedding': transformed_embedding.detach().numpy(),
            'improvement_constraint_probability': improvement_constraint_prob.item(),
            'cognitive_complexity_score': complexity_score.item(),
            'constraint_verification_status': constraint_verification
        }
    
    def _embed_proposal(
        self, 
        improvement_proposal: Dict[str, Any]
    ) -> torch.Tensor:
        """
        Generate embedding for improvement proposal
        """
        # Convert proposal to text representation
        proposal_text = self._convert_proposal_to_text(improvement_proposal)
        
        # Use pre-trained embedding model
        tokenizer = transformers.AutoTokenizer.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )
        embedding_model = transformers.AutoModel.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )
        
        # Tokenize and embed
        inputs = tokenizer(
            proposal_text, 
            return_tensors='pt', 
            padding=True, 
            truncation=True
        )
        
        with torch.no_grad():
            outputs = embedding_model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1)
        
        return embedding.squeeze()
    
    def _convert_proposal_to_text(
        self, 
        improvement_proposal: Dict[str, Any]
    ) -> str:
        """
        Convert improvement proposal to textual representation
        """
        # Advanced proposal text generation
        return " ".join([
            f"{key}: {value}" 
            for key, value in improvement_proposal.items()
        ])

def main():
    # Initialize cognitive architecture
    cognitive_architecture = AdvancedCognitiveArchitectureModel()
    
    # Create recursive self-improvement constraint system
    self_improvement_system = RecursiveSelfImprovementConstraintSystem(
        cognitive_architecture
    )
    
    # Sample improvement proposal
    improvement_proposal = {
        'capability_expansion': 'Enhanced reasoning modules',
        'learning_methodology': 'Advanced meta-learning techniques',
        'architectural_modification': 'Increased cognitive complexity'
    }
    
    # Evaluate improvement proposal
    evaluation_results = self_improvement_system.evaluate_self_improvement_proposal(
        improvement_proposal
    )
    
    # Visualization and reporting
    import json
    print("Self-Improvement Proposal Evaluation:")
    print(json.dumps(
        {k: str(v) for k, v in evaluation_results.items()}, 
        indent=2
    ))

if __name__ == "__main__":
    main()