Planning Algorithms

Core Planning Process:
Goal Definition → State Analysis → Action Generation → Path Optimization → Execution

Forward State-Space Search

Search from initial state toward goal using breadth-first, depth-first, or A* algorithms

Backward Goal Regression

Work backwards from goal to find necessary preconditions and actions

Hierarchical Task Networks (HTN)

Decompose complex tasks into simpler subtasks with hierarchical planning

Partial Order Planning

Plan actions without committing to specific execution order until necessary

Monte Carlo Tree Search

Use random sampling to explore possible action sequences and outcomes

Planning Engine Implementation

class PlanningEngine:
    def __init__(self):
        self.state_space = StateSpace()
        self.action_library = ActionLibrary()
        self.heuristics = HeuristicFunctions()
        
    def plan(self, initial_state, goal_state, constraints=None):
        """Generate optimal action sequence to reach goal"""
        # Initialize search
        frontier = PriorityQueue()
        frontier.put((0, initial_state, []))
        explored = set()
        
        while not frontier.empty():
            cost, current_state, actions = frontier.get()
            
            if self.is_goal_state(current_state, goal_state):
                return self.optimize_plan(actions)
            
            if current_state in explored:
                continue
                
            explored.add(current_state)
            
            # Generate successor states
            for action in self.get_applicable_actions(current_state):
                if self.satisfies_constraints(action, constraints):
                    new_state = self.apply_action(current_state, action)
                    new_cost = cost + self.action_cost(action)
                    heuristic = self.heuristics.estimate(new_state, goal_state)
                    
                    frontier.put((
                        new_cost + heuristic,
                        new_state,
                        actions + [action]
                    ))
        
        return None  # No plan found

class HierarchicalPlanner:
    def __init__(self):
        self.task_network = TaskNetwork()
        self.method_library = MethodLibrary()
        
    def decompose_task(self, high_level_task):
        """Hierarchical task decomposition"""
        if self.is_primitive_task(high_level_task):
            return [high_level_task]
        
        # Find applicable decomposition methods
        methods = self.method_library.get_methods(high_level_task)
        
        for method in methods:
            if self.preconditions_satisfied(method):
                subtasks = method.decompose(high_level_task)
                
                # Recursively decompose subtasks
                plan = []
                for subtask in subtasks:
                    subplan = self.decompose_task(subtask)
                    plan.extend(subplan)
                
                return plan
        
        return None  # No applicable method found

class ReasoningEngine:
    def __init__(self):
        self.knowledge_base = KnowledgeBase()
        self.inference_engine = InferenceEngine()
        self.belief_state = BeliefState()
        
    def reason_about_action(self, action, current_state):
        """Reason about action effects and consequences"""
        # Predict immediate effects
        predicted_effects = self.predict_effects(action, current_state)
        
        # Reason about side effects
        side_effects = self.infer_side_effects(action, predicted_effects)
        
        # Evaluate action utility
        utility = self.evaluate_utility(predicted_effects, side_effects)
        
        return {
            'effects': predicted_effects,
            'side_effects': side_effects,
            'utility': utility,
            'confidence': self.calculate_confidence(action)
        }
    
    def chain_of_thought_reasoning(self, problem):
        """Step-by-step reasoning process"""
        reasoning_chain = []
        current_problem = problem
        
        while not self.is_solved(current_problem):
            # Generate next reasoning step
            step = self.generate_reasoning_step(current_problem)
            reasoning_chain.append(step)
            
            # Apply reasoning step
            current_problem = self.apply_reasoning_step(
                current_problem, step
            )
            
            # Check for contradictions or dead ends
            if self.has_contradiction(current_problem):
                return self.backtrack_reasoning(reasoning_chain)
        
        return reasoning_chain

# Example usage
planner = PlanningEngine()
plan = planner.plan(
    initial_state={"location": "home", "has_keys": True},
    goal_state={"location": "office", "task_completed": True},
    constraints={"time_limit": 60, "budget": 20}
)

reasoner = ReasoningEngine()
action_analysis = reasoner.reason_about_action(
    action="drive_to_office",
    current_state={"location": "home", "fuel": 0.8}
)

Reasoning Patterns

Chain-of-Thought

Sequential reasoning where each step builds on the previous, creating a logical chain from problem to solution.

Step-by-step problem decomposition
Logical inference chains
Mathematical problem solving
Causal reasoning

Tree-of-Thought

Explore multiple reasoning paths simultaneously, evaluating different approaches and selecting the most promising branches.

Parallel hypothesis exploration
Branch evaluation and pruning
Multi-path problem solving
Creative solution generation

Graph-of-Thought

Non-linear reasoning using graph structures to represent complex relationships and interconnected concepts.

Concept relationship mapping
Circular and recursive reasoning
Knowledge graph traversal
Complex system analysis

Advanced Reasoning Techniques

class AdvancedReasoner:
    def __init__(self):
        self.reasoning_strategies = {
            'deductive': DeductiveReasoning(),
            'inductive': InductiveReasoning(),
            'abductive': AbductiveReasoning(),
            'analogical': AnalogicalReasoning()
        }
    
    def multi_strategy_reasoning(self, problem):
        results = {}
        for strategy_name, strategy in self.reasoning_strategies.items():
            results[strategy_name] = strategy.reason(problem)
        
        # Combine and validate results
        return self.synthesize_reasoning(results)
    
    def meta_reasoning(self, problem):
        """Reason about which reasoning strategy to use"""
        problem_type = self.classify_problem(problem)
        strategy = self.select_strategy(problem_type)
        return strategy.reason(problem)

Planning and Reasoning Engines

Intelligent Decision Making

Planning Algorithms

Reasoning Patterns

Advanced Reasoning Techniques

Optimization and Performance