⚙️ Session 9: Advanced Planning - Sophisticated HTN & Reflection Systems¶

class AdvancedConstraintHTNPlanner(DataHTNPlanner):
    """Sophisticated HTN planner with constraint satisfaction and optimization"""

    def __init__(self, agent, domain_knowledge: Dict[str, Any]):
        super().__init__(agent, domain_knowledge)
        self.constraint_solver = ConstraintSolver()
        self.optimization_engine = PlanOptimizationEngine()
        self.resource_manager = ResourceManager()
        self.plan_validator = PlanValidator()

    async def create_constrained_hierarchical_plan(
        self, data_goal: str, initial_state: Dict[str, Any],
        constraints: List[Dict[str, Any]], optimization_objectives: Dict[str, float]
    ) -> Dict[str, Any]:
        """Create hierarchical plan satisfying complex constraints and optimization goals"""

        # Phase 1: Enhanced goal analysis with constraint integration
        enhanced_goal_analysis = await self._analyze_constrained_goal(
            data_goal, initial_state, constraints
        )

        if not enhanced_goal_analysis['feasible']:
            return {
                'plan_generated': False,
                'reason': 'Goal infeasible given constraints',
                'infeasibility_analysis': enhanced_goal_analysis['infeasibility_reasons']
            }

        # Phase 2: Constraint-aware task decomposition
        constrained_decomposition = await self._constrained_task_decomposition(
            enhanced_goal_analysis['root_task'], initial_state, constraints
        )

        # Phase 3: Multi-objective optimization
        optimized_plan = await self._multi_objective_plan_optimization(
            constrained_decomposition['candidate_plans'], optimization_objectives
        )

        # Phase 4: Resource allocation and scheduling
        resource_schedule = await self._create_resource_schedule(
            optimized_plan['optimal_plan'], constraints
        )

        # Phase 5: Plan validation and risk assessment
        validation_result = await self._validate_constrained_plan(
            optimized_plan['optimal_plan'], resource_schedule, constraints
        )

        return {
            'plan_generated': validation_result['valid'],
            'hierarchical_plan': optimized_plan['optimal_plan'] if validation_result['valid'] else None,
            'resource_schedule': resource_schedule if validation_result['valid'] else None,
            'optimization_scores': optimized_plan['objective_scores'],
            'constraint_satisfaction': validation_result['constraint_compliance'],
            'risk_assessment': validation_result['risk_factors'],
            'alternative_plans': optimized_plan.get('alternative_plans', [])
        }

async def _constrained_task_decomposition(
    self, root_task: DataTask, initial_state: Dict[str, Any],
    constraints: List[Dict[str, Any]]
) -> Dict[str, Any]:
    """Decompose tasks while propagating and satisfying constraints"""

    decomposition_search = ConstrainedDecompositionSearch()
    candidate_plans = []

    # Create constraint propagation context
    constraint_context = await self._create_constraint_context(constraints, initial_state)

    # Initialize search with root task
    search_stack = [(root_task, constraint_context, [])]
    max_search_iterations = 1000
    iteration_count = 0

    while search_stack and iteration_count < max_search_iterations:
        current_task, current_constraints, partial_plan = search_stack.pop()
        iteration_count += 1

        if current_task.task_type == DataTaskType.PRIMITIVE:
            # Check if primitive task satisfies constraints
            constraint_check = await self._check_primitive_constraints(
                current_task, current_constraints
            )

            if constraint_check['satisfiable']:
                # Complete plan found
                complete_plan = partial_plan + [current_task]
                plan_quality = await self._evaluate_plan_quality(
                    complete_plan, constraint_context
                )

                candidate_plans.append({
                    'plan': complete_plan,
                    'quality_score': plan_quality['overall_score'],
                    'constraint_violations': constraint_check['violations'],
                    'resource_utilization': plan_quality['resource_efficiency']
                })

        elif current_task.task_type == DataTaskType.COMPOUND:
            # Find applicable decompositions
            applicable_decompositions = await self._find_constrained_decompositions(
                current_task, current_constraints
            )

            # Add each decomposition to search space
            for decomposition in applicable_decompositions:
                # Propagate constraints through decomposition
                propagated_constraints = await self._propagate_constraints(
                    decomposition, current_constraints
                )

                # Add subtasks to search stack
                for subtask in reversed(decomposition.subtasks):
                    search_stack.append((
                        subtask,
                        propagated_constraints,
                        partial_plan.copy()
                    ))

    # Sort candidate plans by quality and constraint satisfaction
    ranked_plans = sorted(
        candidate_plans,
        key=lambda p: (len(p['constraint_violations']), -p['quality_score'])
    )

    return {
        'candidate_plans': ranked_plans,
        'search_statistics': {
            'iterations': iteration_count,
            'plans_found': len(candidate_plans),
            'constraint_context': constraint_context
        }
    }

async def _multi_objective_plan_optimization(
    self, candidate_plans: List[Dict], optimization_objectives: Dict[str, float]
) -> Dict[str, Any]:
    """Optimize plans across multiple objectives using Pareto optimization"""

    if not candidate_plans:
        return {'optimal_plan': None, 'reason': 'No candidate plans available'}

    # Calculate objective scores for each plan
    plan_evaluations = []
    for plan_candidate in candidate_plans:
        objective_scores = {}

        # Performance objective
        if 'performance' in optimization_objectives:
            objective_scores['performance'] = await self._evaluate_plan_performance(
                plan_candidate['plan']
            )

        # Resource efficiency objective
        if 'resource_efficiency' in optimization_objectives:
            objective_scores['resource_efficiency'] = await self._evaluate_resource_efficiency(
                plan_candidate['plan']
            )

        # Quality assurance objective
        if 'quality_assurance' in optimization_objectives:
            objective_scores['quality_assurance'] = await self._evaluate_quality_assurance(
                plan_candidate['plan']
            )

        # Risk minimization objective
        if 'risk_minimization' in optimization_objectives:
            objective_scores['risk_minimization'] = await self._evaluate_risk_minimization(
                plan_candidate['plan']
            )

        # Adaptability objective
        if 'adaptability' in optimization_objectives:
            objective_scores['adaptability'] = await self._evaluate_plan_adaptability(
                plan_candidate['plan']
            )

        plan_evaluations.append({
            'plan': plan_candidate['plan'],
            'objective_scores': objective_scores,
            'constraint_violations': plan_candidate['constraint_violations'],
            'base_quality': plan_candidate['quality_score']
        })

    # Apply Pareto optimization
    pareto_optimal_plans = await self._find_pareto_optimal_plans(
        plan_evaluations, optimization_objectives
    )

    # Select best plan using weighted objective combination
    best_plan = await self._select_best_plan_weighted(
        pareto_optimal_plans, optimization_objectives
    )

    return {
        'optimal_plan': best_plan['plan'],
        'objective_scores': best_plan['objective_scores'],
        'pareto_optimal_plans': pareto_optimal_plans,
        'alternative_plans': [p for p in pareto_optimal_plans if p != best_plan]
    }

class PredictiveReplanningSystem(DynamicDataReplanner):
    """Advanced replanning system with predictive failure modeling and proactive adaptation"""

    def __init__(self, htn_planner: AdvancedConstraintHTNPlanner):
        super().__init__(htn_planner)
        self.failure_predictor = FailurePredictionModel()
        self.adaptation_strategy_engine = AdaptationStrategyEngine()
        self.proactive_monitoring = ProactiveMonitoringSystem()

    async def execute_with_predictive_replanning(
        self, data_plan: List[DataTask], initial_state: Dict[str, Any],
        risk_tolerance: float = 0.7
    ) -> Dict[str, Any]:
        """Execute plan with predictive failure modeling and proactive replanning"""

        current_state = initial_state.copy()
        remaining_tasks = data_plan.copy()
        completed_tasks = []
        execution_trace = []
        proactive_replans = 0

        # Initialize predictive monitoring
        await self.proactive_monitoring.start_monitoring(data_plan, current_state)

        while remaining_tasks and self.monitoring_active:
            current_task = remaining_tasks[0]

            # Predictive failure analysis
            failure_prediction = await self.failure_predictor.predict_task_failure(
                current_task, current_state, remaining_tasks
            )

            # Proactive replanning decision
            if failure_prediction['failure_probability'] > (1.0 - risk_tolerance):
                proactive_replan_result = await self._execute_proactive_replanning(
                    current_task, remaining_tasks, current_state, failure_prediction
                )

                if proactive_replan_result['success']:
                    remaining_tasks = proactive_replan_result['new_plan']
                    proactive_replans += 1
                    execution_trace.append(('proactive_replan', proactive_replan_result))
                    continue

            # Standard execution with enhanced monitoring
            execution_result = await self._execute_with_enhanced_monitoring(
                current_task, current_state, failure_prediction
            )

            execution_trace.append(('task_execution', execution_result))

            if execution_result['success']:
                # Update state and continue
                current_state = self._apply_task_effects(
                    current_task, current_state, execution_result
                )
                completed_tasks.append(current_task)
                remaining_tasks.pop(0)

                # Update predictive models with successful execution
                await self.failure_predictor.update_success_model(
                    current_task, execution_result, current_state
                )
            else:
                # Reactive replanning with failure analysis
                reactive_replan = await self._execute_reactive_replanning(
                    current_task, remaining_tasks, current_state, execution_result
                )

                if reactive_replan['success']:
                    remaining_tasks = reactive_replan['new_plan']
                    execution_trace.append(('reactive_replan', reactive_replan))
                    continue
                else:
                    execution_trace.append(('execution_failure', reactive_replan))
                    break

        return {
            'execution_completed': len(remaining_tasks) == 0,
            'completed_tasks': completed_tasks,
            'remaining_tasks': remaining_tasks,
            'final_state': current_state,
            'execution_trace': execution_trace,
            'proactive_replans': proactive_replans,
            'predictive_accuracy': await self._calculate_prediction_accuracy(execution_trace)
        }

class FailurePredictionModel:
    """Machine learning model for predicting task execution failures"""

    def __init__(self):
        self.historical_executions = []
        self.failure_patterns = {}
        self.success_patterns = {}
        self.feature_extractor = ExecutionFeatureExtractor()
        self.ml_model = GradientBoostingFailurePredictor()

    async def predict_task_failure(
        self, task: DataTask, current_state: Dict[str, Any],
        remaining_tasks: List[DataTask]
    ) -> Dict[str, Any]:
        """Predict probability and type of task execution failure"""

        # Extract comprehensive features
        task_features = await self._extract_task_features(task, current_state)
        context_features = await self._extract_context_features(
            current_state, remaining_tasks
        )
        historical_features = await self._extract_historical_features(
            task, current_state
        )

        combined_features = {
            **task_features,
            **context_features,
            **historical_features
        }

        # Generate failure probability prediction
        failure_probability = await self.ml_model.predict_failure_probability(
            combined_features
        )

        # Predict most likely failure types
        failure_types = await self.ml_model.predict_failure_types(
            combined_features
        )

        # Generate failure scenario analysis
        failure_scenarios = await self._generate_failure_scenarios(
            task, combined_features, failure_types
        )

        # Calculate confidence intervals
        prediction_confidence = await self._calculate_prediction_confidence(
            combined_features, failure_probability
        )

        return {
            'failure_probability': failure_probability,
            'prediction_confidence': prediction_confidence,
            'likely_failure_types': failure_types,
            'failure_scenarios': failure_scenarios,
            'mitigation_suggestions': await self._suggest_failure_mitigations(
                failure_scenarios, task, current_state
            ),
            'features_used': combined_features
        }

class DeepLearningReflectionEngine(DataReflectionEngine):
    """Advanced reflection engine with deep learning and strategy evolution"""

    def __init__(self, agent):
        super().__init__(agent)
        self.strategy_evolution_engine = StrategyEvolutionEngine()
        self.pattern_deep_analyzer = DeepPatternAnalyzer()
        self.meta_learning_system = MetaLearningSystem()
        self.coordination_optimizer = CoordinationOptimizer()

    async def deep_reflection_analysis(
        self, execution_history: List[Dict[str, Any]],
        coordination_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Perform deep reflection analysis for strategy evolution"""

        # Phase 1: Multi-dimensional execution analysis
        execution_analysis = await self._multi_dimensional_execution_analysis(
            execution_history, coordination_context
        )

        # Phase 2: Deep pattern recognition
        deep_patterns = await self.pattern_deep_analyzer.analyze_coordination_patterns(
            execution_history, coordination_context
        )

        # Phase 3: Strategy effectiveness evaluation
        strategy_evaluation = await self._evaluate_strategy_effectiveness(
            execution_analysis, deep_patterns, coordination_context
        )

        # Phase 4: Meta-learning integration
        meta_insights = await self.meta_learning_system.extract_meta_insights(
            strategy_evaluation, self.data_experience_buffer
        )

        # Phase 5: Strategy evolution recommendations
        evolution_recommendations = await self.strategy_evolution_engine.generate_evolution_strategies(
            strategy_evaluation, meta_insights, deep_patterns
        )

        # Phase 6: Coordination optimization
        coordination_optimizations = await self.coordination_optimizer.optimize_coordination_patterns(
            execution_history, evolution_recommendations
        )

        return {
            'execution_analysis': execution_analysis,
            'deep_patterns': deep_patterns,
            'strategy_evaluation': strategy_evaluation,
            'meta_insights': meta_insights,
            'evolution_recommendations': evolution_recommendations,
            'coordination_optimizations': coordination_optimizations,
            'implementation_priority': await self._prioritize_improvements(
                evolution_recommendations, coordination_optimizations
            )
        }

class StrategyEvolutionEngine:
    """Evolves coordination strategies based on reflection insights"""

    def __init__(self):
        self.genetic_algorithm = CoordinationGeneticAlgorithm()
        self.strategy_mutator = StrategyMutationEngine()
        self.fitness_evaluator = StrategyFitnessEvaluator()

    async def generate_evolution_strategies(
        self, strategy_evaluation: Dict, meta_insights: Dict,
        deep_patterns: Dict
    ) -> Dict[str, Any]:
        """Generate evolved coordination strategies using advanced algorithms"""

        # Extract current strategy genome
        current_strategy_genome = await self._extract_strategy_genome(
            strategy_evaluation, meta_insights
        )

        # Generate strategy mutations
        mutation_candidates = await self.strategy_mutator.generate_mutations(
            current_strategy_genome, deep_patterns['improvement_vectors']
        )

        # Apply genetic algorithm for strategy evolution
        evolved_strategies = await self.genetic_algorithm.evolve_strategies(
            [current_strategy_genome] + mutation_candidates,
            fitness_function=self._strategy_fitness_function,
            generations=10,
            population_size=20
        )

        # Evaluate evolved strategies
        strategy_evaluations = []
        for strategy in evolved_strategies[:5]:  # Top 5 strategies
            evaluation = await self.fitness_evaluator.comprehensive_evaluation(
                strategy, strategy_evaluation['baseline_performance']
            )
            strategy_evaluations.append({
                'strategy': strategy,
                'evaluation': evaluation,
                'expected_improvement': evaluation['improvement_estimate']
            })

        # Select best evolution candidates
        best_evolution_strategies = sorted(
            strategy_evaluations,
            key=lambda s: s['expected_improvement'],
            reverse=True
        )[:3]

        return {
            'evolved_strategies': best_evolution_strategies,
            'evolution_process_stats': {
                'mutations_generated': len(mutation_candidates),
                'strategies_evolved': len(evolved_strategies),
                'convergence_generations': evolved_strategies[0].get('generation', 0)
            },
            'implementation_recommendations': await self._generate_implementation_recommendations(
                best_evolution_strategies
            )
        }

class MetaLearningSystem:
    """Meta-learning system that learns how to learn more effectively"""

    def __init__(self):
        self.learning_strategy_database = {}
        self.meta_pattern_recognizer = MetaPatternRecognizer()
        self.adaptation_effectiveness_tracker = AdaptationEffectivenessTracker()

    async def extract_meta_insights(
        self, strategy_evaluation: Dict, experience_buffer: List[Dict]
    ) -> Dict[str, Any]:
        """Extract meta-level insights about learning effectiveness"""

        # Analyze learning trajectory patterns
        learning_trajectories = await self._analyze_learning_trajectories(
            experience_buffer
        )

        # Identify successful learning patterns
        successful_learning_patterns = await self._identify_successful_learning_patterns(
            learning_trajectories, strategy_evaluation
        )

        # Evaluate adaptation strategies
        adaptation_effectiveness = await self._evaluate_adaptation_strategies(
            experience_buffer, strategy_evaluation
        )

        # Generate meta-learning insights
        meta_insights = {
            'optimal_learning_rates': await self._calculate_optimal_learning_rates(
                learning_trajectories
            ),
            'effective_adaptation_triggers': await self._identify_adaptation_triggers(
                adaptation_effectiveness
            ),
            'learning_pattern_preferences': successful_learning_patterns,
            'meta_optimization_opportunities': await self._identify_meta_optimization_opportunities(
                learning_trajectories, adaptation_effectiveness
            )
        }

        # Update meta-learning database
        await self._update_meta_learning_database(meta_insights, experience_buffer)

        return meta_insights