Planning with state-space search is a fundamental approach in Artificial Intelligence. It finds a sequence of actions that transforms an initial state of the world into a desired goal state. The process involves representing the problem as a state space and searching through it systematically.
A state space consists of several key components. States represent different configurations of the world. Actions are operations that transform one state into another. The goal test determines whether we have reached a desired state. Path cost measures the cost of reaching a particular state through a sequence of actions.
Several search algorithms can be used to find solutions in state spaces. Breadth-First Search explores states level by level and guarantees finding the optimal solution. Depth-First Search goes deep into the search tree before backtracking, using less memory. A-star search uses heuristics to guide the search more efficiently while maintaining optimality when the heuristic is admissible.
The 8-puzzle is a classic example of planning with state-space search. Each state represents a configuration of numbered tiles with one blank space. Actions involve moving the blank tile in four directions. The goal is to reach a target configuration. Heuristics like Manhattan distance help guide the search efficiently by estimating how far each tile is from its goal position.
Planning with state-space search has numerous applications across various domains. In robotics, it's used for path planning and motion control. Game AI systems use it for strategic decision-making in games like chess. It's also essential in automated planning for task scheduling, navigation systems for route finding, and solving various types of puzzles. While this approach provides systematic and optimal solutions, it faces challenges with large state spaces and requires good heuristics for efficiency.
A state space consists of several key components. States represent different configurations of the world. Actions are operations that transform one state into another. The goal test determines whether we have reached a desired state. Path cost measures the cost of reaching a particular state through a sequence of actions.
Several search algorithms can be used to find solutions in state spaces. Breadth-First Search explores states level by level and guarantees finding the optimal solution. Depth-First Search goes deep into the search tree before backtracking, using less memory. A-star search uses heuristics to guide the search more efficiently while maintaining optimality when the heuristic is admissible.
The 8-puzzle is a classic example of planning with state-space search. Each state represents a configuration of numbered tiles with one blank space. Actions involve moving the blank tile in four directions. The goal is to reach a target configuration. Heuristics like Manhattan distance help guide the search efficiently by estimating how far each tile is from its goal position.
Planning with state-space search has numerous applications across various domains. In robotics, it's used for path planning and motion control. Game AI systems use it for strategic decision-making in games like chess. It's also essential in automated planning for task scheduling, navigation systems for route finding, and solving various types of puzzles. While this approach provides systematic and optimal solutions, it faces challenges with large state spaces and requires good heuristics for efficiency.