State Space Representation
In order to express the problem using state space, the following elements must be defined:
- States: Different arrangements of the issue, frequently shown as graph nodes.
- Initial State: The initial setting that the search starts with.
- Goal State(s): The ideal configuration(s) denoting a resolution.
- Actions: The processes via which a system changes states.
- Transition Model: Explains what happens when states are subjected to actions.
- Path cost: The expense of moving from an initial state to a certain state, expressed as a numerical value linked to each path.
State Space Search in AI
An essential method in artificial intelligence is state space search, which looks for potential states and their transitions to solve issues. According to this method, the problem is modeled as a state space, with each state representing a possible configuration and transitions denoting actions or operations that change the state of the problem. Finding a route that meets predetermined requirements from an initial state to a goal state is the aim.
This article provides an in-depth exploration of state space search in artificial intelligence, detailing its principles, strategies, and applications, with a practical implementation using Breadth-First Search (BFS) to solve the 8-puzzle problem.
Table of Content
- Understanding State Space Search
- Principles and Features of State Space Search
- Steps in State Space Search
- Heuristics in State Space Search
- State Space Representation
- State Space Search: Breadth-First Search (BFS) algorithm on 8-Puzzle Problem
- Applications of State Space Search
- Challenges in State Space Search
- Conclusion