Searching refers to the process of systematically exploring a set of possible states or solutions to find a path from an initial state to a goal state.
Thank you for reading this post, don't forget to subscribe!- It involves examining different configurations (states) of a problem and determining the sequence of actions required to reach the desired outcome.
Problem Solving by Searching is a fundamental technique in AI where an agent explores a set of possible states (or solutions) to find a path from an initial state to a goal state.
- It involves systematically exploring a state space (a representation of all possible states) to find a sequence of actions that lead to the desired goal.
Problem as a State Space Search:
A problem can be represented as a state space, which consists of:
- States: Representations of the problem at different stages.
- Initial State: The starting point of the problem.
- Goal State: The desired outcome or solution.
- Actions: Operations that transform one state into another.
- Transition Model: Describes how actions change the state.
- Path Cost: The cost associated with a sequence of actions (used to evaluate solutions).
The goal is to find a path from the initial state to the goal state with the minimum cost.
Problem Formulation:
Problem Formulation is the process of defining a problem in terms of:
- Initial State: Where the problem starts.
- Goal State: The desired outcome.
- Actions: Possible moves or operations.
- Transition Model: How actions change the state.
- Path Cost: The cost of each action or path.
Example: In the 8-puzzle problem, the initial state is a scrambled grid, the goal state is the solved grid, and actions involve sliding tiles.
Well-Defined Problems:
A well-defined problem has:
- A clear initial state.
- A clear goal state.
- A set of actions with well-defined outcomes.
- A measurable path cost.
Example: Finding the shortest path between two cities on a map.
Solving Problems by Searching:
Solving problems involves exploring the state space to find a sequence of actions that lead from the initial state to the goal state.
This is done using search algorithms, which can be:
- Uninformed Search: No additional information about the problem (e.g., Depth-First Search).
- Informed Search: Uses heuristic information to guide the search (e.g., A* Search).