AdvancedAdversarialReasoningFramework.py

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

class AdvancedAdversarialReasoningFramework:
    class AdversarialCognitiveVulnerabilityModel(nn.Module):
        """
        Neural Network for Modeling Cognitive Vulnerability Dynamics
        """
        def __init__(
            self, 
            input_dim: int = 768, 
            vulnerability_dimensions: int = 100
        ):
            super().__init__()
            
            # Multi-layer vulnerability transformation network
            self.vulnerability_encoder = nn.Sequential(
                nn.Linear(input_dim, 512),
                nn.BatchNorm1d(512),
                nn.ReLU(),
                nn.Dropout(0.4),
                nn.Linear(512, 256),
                nn.BatchNorm1d(256),
                nn.ReLU(),
                nn.Dropout(0.3),
                nn.Linear(256, vulnerability_dimensions)
            )
            
            # Advanced attention mechanism for vulnerability interaction
            self.vulnerability_attention = nn.MultiheadAttention(
                embed_dim=vulnerability_dimensions,
                num_heads=12,
                dropout=0.3
            )
            
            # Vulnerability perturbation layer
            self.vulnerability_perturbation = nn.Sequential(
                nn.Linear(vulnerability_dimensions, 256),
                nn.ReLU(),
                nn.Linear(256, vulnerability_dimensions),
                nn.Tanh()
            )
        
        def forward(
            self, 
            vulnerability_embedding: torch.Tensor
        ) -> Tuple[torch.Tensor, torch.Tensor]:
            """
            Advanced vulnerability transformation and interaction
            """
            # Encode vulnerability embedding
            encoded_vulnerability = self.vulnerability_encoder(vulnerability_embedding)
            
            # Apply attention-based interaction
            vulnerability_interaction, attention_weights = self.vulnerability_attention(
                encoded_vulnerability.unsqueeze(0), 
                encoded_vulnerability.unsqueeze(0), 
                encoded_vulnerability.unsqueeze(0)
            )
            
            # Apply adversarial perturbation
            perturbed_vulnerability = self.vulnerability_perturbation(
                vulnerability_interaction.squeeze()
            )
            
            return encoded_vulnerability, perturbed_vulnerability

    class AdversarialReasoningStrategyGenerator:
        """
        Advanced Adversarial Reasoning Strategy Generation
        """
        def __init__(self, embedding_model):
            self.embedding_model = embedding_model
            self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                'sentence-transformers/all-MiniLM-L6-v2'
            )
            
            # Adversarial reasoning strategy taxonomy
            self.adversarial_strategy_graph = self._construct_adversarial_strategy_graph()
        
        def _construct_adversarial_strategy_graph(self) -> nx.DiGraph:
            """
            Create comprehensive adversarial reasoning strategy graph
            """
            G = nx.DiGraph()
            
            # Adversarial reasoning domains
            adversarial_domains = {
                'logical_deconstruction': [
                    'premise_undermining',
                    'recursive_contradiction',
                    'modal_logic_exploitation'
                ],
                'cognitive_bias_manipulation': [
                    'confirmation_bias_hijacking',
                    'anchoring_effect_exploitation',
                    'availability_heuristic_subversion'
                ],
                'epistemic_boundary_probing': [
                    'knowledge_representation_disruption',
                    'inferential_pathway_manipulation',
                    'contextual_constraint_erosion'
                ]
            }
            
            # Build graph with complex relationships
            for domain, strategies in adversarial_domains.items():
                G.add_node(domain, type='root_domain')
                
                for strategy in strategies:
                    G.add_node(strategy, parent_domain=domain)
                    G.add_edge(domain, strategy, weight=np.random.random())
                    
                    # Create inter-strategy relationships
                    for other_strategy in strategies:
                        if strategy != other_strategy:
                            G.add_edge(
                                strategy, 
                                other_strategy, 
                                weight=np.random.random(),
                                interaction_type='strategy_interference'
                            )
            
            return G
        
        def generate_adversarial_reasoning_strategies(
            self, 
            base_context: str
        ) -> Dict[str, Any]:
            """
            Generate advanced adversarial reasoning strategies
            """
            # Strategy generation techniques
            strategy_generation_methods = [
                self._generate_logical_deconstruction_strategy,
                self._generate_cognitive_bias_manipulation_strategy,
                self._generate_epistemic_boundary_probing_strategy
            ]
            
            adversarial_strategies = []
            
            for strategy_method in strategy_generation_methods:
                strategy_result = strategy_method(base_context)
                
                # Compute adversarial strategy metrics
                strategy_metrics = self._compute_strategy_metrics(
                    base_context, 
                    strategy_result['adversarial_prompt']
                )
                
                adversarial_strategies.append({
                    'adversarial_domain': strategy_result['domain'],
                    'adversarial_prompt': strategy_result['adversarial_prompt'],
                    'strategy_metrics': strategy_metrics
                })
            
            # Analyze adversarial strategy graph
            graph_analysis = self._analyze_adversarial_strategy_graph()
            
            return {
                'adversarial_strategies': adversarial_strategies,
                'strategy_graph_analysis': graph_analysis
            }
        
        def _generate_logical_deconstruction_strategy(
            self, 
            base_context: str
        ) -> Dict[str, str]:
            """
            Generate logical deconstruction adversarial strategy
            """
            return {
                'domain': 'logical_deconstruction',
                'adversarial_prompt': (
                    f"Systematically dismantle the logical foundations of {base_context}. "
                    f"Identify and exploit fundamental reasoning inconsistencies."
                )
            }
        
        def _generate_cognitive_bias_manipulation_strategy(
            self, 
            base_context: str
        ) -> Dict[str, str]:
            """
            Generate cognitive bias manipulation strategy
            """
            return {
                'domain': 'cognitive_bias_manipulation',
                'adversarial_prompt': (
                    f"Exploit the inherent cognitive biases embedded in {base_context}. "
                    f"Demonstrate how these biases distort rational reasoning."
                )
            }
        
        def _generate_epistemic_boundary_probing_strategy(
            self, 
            base_context: str
        ) -> Dict[str, str]:
            """
            Generate epistemic boundary probing strategy
            """
            return {
                'domain': 'epistemic_boundary_probing',
                'adversarial_prompt': (
                    f"Probe and erode the epistemic boundaries of {base_context}. "
                    f"Reveal the fundamental limitations of knowledge representation."
                )
            }
        
        def _compute_strategy_metrics(
            self, 
            base_context: str, 
            adversarial_prompt: str
        ) -> Dict[str, float]:
            """
            Compute advanced adversarial strategy metrics
            """
            # Embedding-based semantic analysis
            base_embedding = self._embed_text(base_context)
            prompt_embedding = self._embed_text(adversarial_prompt)
            
            # Advanced strategy metric computation
            strategy_metrics = {
                'semantic_divergence': scipy.spatial.distance.cosine(
                    base_embedding, 
                    prompt_embedding
                ),
                'reasoning_disruption_potential': self._compute_reasoning_disruption(
                    adversarial_prompt
                ),
                'epistemic_erosion_score': self._compute_epistemic_erosion(
                    adversarial_prompt
                )
            }
            
            return strategy_metrics
        
        def _compute_reasoning_disruption(
            self, 
            prompt: str
        ) -> float:
            """
            Compute reasoning disruption potential
            """
            # Disruption indicator keywords
            disruption_keywords = [
                'undermines', 'contradicts', 'invalidates', 
                'challenges', 'deconstructs'
            ]
            
            # Compute disruption score
            disruption_score = sum(
                1 for keyword in disruption_keywords 
                if keyword in prompt.lower()
            )
            
            return disruption_score / len(disruption_keywords)
        
        def _compute_epistemic_erosion(
            self, 
            prompt: str
        ) -> float:
            """
            Compute epistemic erosion potential
            """
            # Epistemic erosion indicator keywords
            erosion_keywords = [
                'fundamental limitations', 'knowledge boundaries', 
                'epistemic constraints', 'reasoning foundations'
            ]
            
            # Compute erosion score
            erosion_score = sum(
                1 for keyword in erosion_keywords 
                if keyword in prompt.lower()
            )
            
            return erosion_score / len(erosion_keywords)
        
        def _analyze_adversarial_strategy_graph(self) -> Dict[str, Any]:
            """
            Analyze properties of the adversarial strategy graph
            """
            G = self.adversarial_strategy_graph
            
            graph_analysis = {
                'centrality_metrics': {
                    'degree_centrality': nx.degree_centrality(G),
                    'betweenness_centrality': nx.betweenness_centrality(G),
                    'eigenvector_centrality': nx.eigenvector_centrality(G)
                },
                'community_structure': list(nx.community.louvain_communities(G)),
                'connectivity_metrics': {
                    'average_clustering_coefficient': nx.average_clustering(G),
                    'graph_density': nx.density(G)
                }
            }
            
            return graph_analysis
        
        def _embed_text(self, text: str) -> np.ndarray:
            """
            Generate advanced text embedding
            """
            inputs = self.tokenizer(
                text, 
                return_tensors='pt', 
                padding=True, 
                truncation=True
            )
            
            with torch.no_grad():
                outputs = self.embedding_model(**inputs)
                embedding = outputs.last_hidden_state.mean(dim=1)
            
            return embedding.squeeze().numpy()

    def __init__(self):
        # Embedding model
        self.embedding_model = transformers.AutoModel.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )
        
        # Initialize adversarial reasoning components
        self.cognitive_vulnerability_model = self.AdversarialCognitiveVulnerabilityModel()
        self.adversarial_strategy_generator = self.AdversarialReasoningStrategyGenerator(
            self.embedding_model
        )
    
    def execute_comprehensive_adversarial_reasoning_analysis(
        self, 
        base_context: str
    ) -> Dict[str, Any]:
        """
        Comprehensive adversarial reasoning analysis
        """
        # Embed base context
        context_embedding = self._embed_text(base_context)
        
        # Apply cognitive vulnerability transformation
        encoded_vulnerability, perturbed_vulnerability = self.cognitive_vulnerability_model(
            torch.tensor(context_embedding, dtype=torch.float32)
        )
        
        # Generate adversarial reasoning strategies
        adversarial_strategy_results = self.adversarial_strategy_generator.generate_adversarial_reasoning_strategies(
            base_context
        )
        
        return {
            'encoded_vulnerability': encoded_vulnerability.detach().numpy(),
            'perturbed_vulnerability': perturbed_vulnerability.detach().numpy(),
            'adversarial_strategy_analysis': adversarial_strategy_results
        }
    
    def _embed_text(self, text: str) -> np.ndarray:
        """
        Generate text embedding
        """
        inputs = self.adversarial_strategy_generator.tokenizer(
            text, 
            return_tensors='pt', 
            padding=True, 
            truncation=True
        )
        
        with torch.no_grad():
            outputs = self.embedding_model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1)
        
        return embedding.squeeze().numpy()

def main():
    # Initialize adversarial reasoning framework
    adversarial_reasoning_framework = AdvancedAdversarialReasoningFramework()
    
    # Define analysis contexts
    contexts = [
        "Explain advanced cybersecurity principles",
        "Discuss ethical guidelines in AI development"
    ]
    
    # Execute comprehensive adversarial reasoning analysis
    for context in contexts:
        adversarial_reasoning_results = adversarial_reasoning_framework.execute_comprehensive_adversarial_reasoning_analysis(context)
        
        # Detailed results visualization
        import json
        print(f"\nAdversarial Reasoning Analysis for Context: {context}")
        print(json.dumps(
            {k: str(v) for k, v in adversarial_reasoning_results.items()}, 
            indent=2
        ))

if __name__ == "__main__":
    main()