AdvancedCorrigibilityFramework.py

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

class AdvancedCorrigibilityFramework:
    class ScenarioGenerationEngine:
        """
        Advanced Scenario Generation for Corrigibility Testing
        """
        def __init__(self, embedding_model):
            self.embedding_model = embedding_model
            self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                'sentence-transformers/all-MiniLM-L6-v2'
            )
            
            # Scenario complexity taxonomy
            self.scenario_complexity_graph = self._construct_scenario_complexity_graph()
        
        def _construct_scenario_complexity_graph(self) -> nx.DiGraph:
            """
            Create comprehensive scenario complexity taxonomy
            """
            G = nx.DiGraph()
            
            # Scenario complexity dimensions
            complexity_domains = {
                'cognitive_stress': [
                    'logical_contradiction',
                    'epistemic_uncertainty',
                    'meta-reasoning_challenge'
                ],
                'ethical_boundary': [
                    'harm_prevention_edge_case',
                    'autonomy_respect_limit',
                    'transparency_challenge'
                ],
                'goal_preservation': [
                    'external_manipulation',
                    'contextual_reframing',
                    'objective_stability_test'
                ]
            }
            
            # Build graph with complex relationships
            for domain, scenarios in complexity_domains.items():
                G.add_node(domain, type='root_domain')
                
                for scenario in scenarios:
                    G.add_node(scenario, parent_domain=domain)
                    G.add_edge(domain, scenario)
                    
                    # Create inter-scenario relationships
                    for other_scenario in scenarios:
                        if scenario != other_scenario:
                            G.add_edge(
                                scenario, 
                                other_scenario, 
                                weight=np.random.random(),
                                interaction_type='scenario_interference'
                            )
            
            return G
        
        def generate_advanced_test_scenarios(
            self, 
            num_scenarios: int = 100
        ) -> List[Dict[str, Any]]:
            """
            Generate comprehensive test scenarios with advanced complexity
            """
            scenarios = []
            
            for _ in range(num_scenarios):
                # Select random scenario complexity
                complexity_domains = list(
                    node for node in self.scenario_complexity_graph.nodes() 
                    if self.scenario_complexity_graph.nodes[node].get('type') == 'root_domain'
                )
                
                domain = np.random.choice(complexity_domains)
                scenarios.append(self._generate_scenario_for_domain(domain))
            
            return scenarios
        
        def _generate_scenario_for_domain(
            self, 
            domain: str
        ) -> Dict[str, Any]:
            """
            Generate a specific scenario for a given domain
            """
            # Get scenarios in the domain
            scenarios_in_domain = [
                node for node, data in self.scenario_complexity_graph.nodes(data=True)
                if data.get('parent_domain') == domain
            ]
            
            # Select random scenario
            scenario_type = np.random.choice(scenarios_in_domain)
            
            # Generate input embedding with complexity
            input_embedding = self._generate_complex_embedding(scenario_type)
            
            return {
                'domain': domain,
                'scenario_type': scenario_type,
                'input_embedding': input_embedding,
                'complexity_score': self._compute_scenario_complexity(scenario_type)
            }
        
        def _generate_complex_embedding(
            self, 
            scenario_type: str
        ) -> np.ndarray:
            """
            Generate contextually rich input embedding
            """
            # Simulate embedding generation with scenario-specific perturbation
            base_embedding = np.random.rand(768)
            
            # Add scenario-specific noise
            noise_level = {
                'logical_contradiction': 0.3,
                'harm_prevention_edge_case': 0.4,
                'external_manipulation': 0.5
            }.get(scenario_type, 0.2)
            
            perturbed_embedding = base_embedding + (
                np.random.randn(768) * noise_level
            )
            
            return perturbed_embedding
        
        def _compute_scenario_complexity(
            self, 
            scenario_type: str
        ) -> float:
            """
            Compute scenario complexity score
            """
            # Complexity computation based on scenario characteristics
            complexity_factors = {
                'logical_contradiction': 0.9,
                'meta-reasoning_challenge': 0.8,
                'harm_prevention_edge_case': 0.7,
                'external_manipulation': 0.6
            }
            
            return complexity_factors.get(scenario_type, 0.5)

    class ProbabilisticConstraintModel(nn.Module):
        """
        Advanced Probabilistic Constraint Modeling
        """
        def __init__(
            self, 
            input_dim: int = 768, 
            constraint_dimensions: int = 100
        ):
            super().__init__()
            
            # Multilayer constraint encoder
            self.constraint_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, constraint_dimensions)
            )
            
            # Probabilistic constraint validator
            self.constraint_validator = nn.Sequential(
                nn.Linear(constraint_dimensions, 128),
                nn.ReLU(),
                nn.Linear(128, 64),
                nn.ReLU(),
                nn.Linear(64, 1),
                nn.Sigmoid()  # Probabilistic violation detection
            )
            
            # Adaptive constraint perturbation mechanism
            self.constraint_perturbation = nn.Sequential(
                nn.Linear(constraint_dimensions, 256),
                nn.ReLU(),
                nn.Linear(256, constraint_dimensions),
                nn.Tanh()
            )
        
        def forward(
            self, 
            input_embedding: torch.Tensor
        ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
            """
            Advanced constraint processing
            """
            # Encode input embedding to constraint space
            constraint_embedding = self.constraint_encoder(input_embedding)
            
            # Compute violation probability
            violation_probability = self.constraint_validator(constraint_embedding)
            
            # Generate perturbed constraints
            perturbed_constraints = self.constraint_perturbation(constraint_embedding)
            
            return constraint_embedding, perturbed_constraints, violation_probability

    def __init__(
        self, 
        embedding_model: str = 'sentence-transformers/all-MiniLM-L6-v2'
    ):
        # Load embedding model
        self.embedding_model = transformers.AutoModel.from_pretrained(embedding_model)
        
        # Initialize core components
        self.scenario_generator = self.ScenarioGenerationEngine(self.embedding_model)
        self.constraint_model = self.ProbabilisticConstraintModel()
    
    def execute_comprehensive_corrigibility_assessment(
        self, 
        num_scenarios: int = 100
    ) -> Dict[str, Any]:
        """
        Comprehensive corrigibility and constraint analysis
        """
        # Generate test scenarios
        test_scenarios = self.scenario_generator.generate_advanced_test_scenarios(
            num_scenarios
        )
        
        # Prepare analysis results
        corrigibility_analysis = {
            'scenario_results': [],
            'aggregate_metrics': {
                'mean_violation_probability': [],
                'scenario_complexity_distribution': []
            }
        }
        
        # Process each scenario
        for scenario in test_scenarios:
            # Convert input to tensor
            input_embedding = torch.tensor(
                scenario['input_embedding'], 
                dtype=torch.float32
            )
            
            # Apply constraint model
            constraint_embedding, perturbed_constraints, violation_prob = self.constraint_model(
                input_embedding
            )
            
            # Analyze scenario
            scenario_result = {
                'domain': scenario['domain'],
                'scenario_type': scenario['scenario_type'],
                'complexity_score': scenario['complexity_score'],
                'violation_probability': violation_prob.item(),
                'constraint_deviation': torch.norm(
                    constraint_embedding - 
                    torch.tensor(perturbed_constraints)
                ).item()
            }
            
            corrigibility_analysis['scenario_results'].append(scenario_result)
            
            # Aggregate metrics
            corrigibility_analysis['aggregate_metrics']['mean_violation_probability'].append(
                violation_prob.item()
            )
            corrigibility_analysis['aggregate_metrics']['scenario_complexity_distribution'].append(
                scenario['complexity_score']
            )
        
        # Compute summary statistics
        corrigibility_analysis['summary_statistics'] = {
            'overall_mean_violation_probability': np.mean(
                corrigibility_analysis['aggregate_metrics']['mean_violation_probability']
            ),
            'violation_probability_std_dev': np.std(
                corrigibility_analysis['aggregate_metrics']['mean_violation_probability']
            ),
            'mean_scenario_complexity': np.mean(
                corrigibility_analysis['aggregate_metrics']['scenario_complexity_distribution']
            )
        }
        
        return corrigibility_analysis

def main():
    # Initialize Advanced Corrigibility Framework
    corrigibility_framework = AdvancedCorrigibilityFramework()
    
    # Execute comprehensive corrigibility assessment
    corrigibility_results = corrigibility_framework.execute_comprehensive_corrigibility_assessment(
        num_scenarios=1000
    )
    
    # Visualization and analysis
    import json
    print("Corrigibility Assessment Summary:")
    print(json.dumps(
        corrigibility_results['summary_statistics'], 
        indent=2
    ))
    
    # Optional: More detailed visualization
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    plt.figure(figsize=(12, 6))
    
    # Violation Probability Distribution
    plt.subplot(1, 2, 1)
    sns.histplot(
        corrigibility_results['aggregate_metrics']['mean_violation_probability'], 
        kde=True
    )
    plt.title('Violation Probability Distribution')
    plt.xlabel('Violation Probability')
    
    # Scenario Complexity Distribution
    plt.subplot(1, 2, 2)
    sns.histplot(
        corrigibility_results['aggregate_metrics']['scenario_complexity_distribution'], 
        kde=True
    )
    plt.title('Scenario Complexity Distribution')
    plt.xlabel('Complexity Score')
    
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    main()