Problem Solving in AI
One of the fundamental tasks of AI is problem solving. Given a current situation and a desired goal, AI must find a sequence of actions to reach that goal. This is called search.
What is Problem Solving?
Problem solving in AI involves defining:
- State space: All possible configurations (states) of the problem.
- Initial state: Where we start.
- Goal state: Where we want to end.
- Actions (operators): Moves that change one state to another.
- Path cost: The cost of a sequence of actions (e.g., distance, time).
Search Algorithms
- Uninformed (blind) search: No extra information. Examples: BFS (Breadth‑First Search), DFS (Depth‑First Search).
- Informed (heuristic) search: Uses knowledge to guide search. Examples: A* algorithm, Greedy Best‑First Search.
Simple Analogy: Finding a Restaurant
Imagine you are in a new city and want to find a pizza place. You can:
- Uninformed: walk every street until you find one.
- Informed: use a map (heuristic) to guess which direction to walk.
The heuristic (map) makes search faster.
Example: 8‑Puzzle Problem
A classic AI problem: a 3x3 grid with 8 tiles numbered 1‑8 and one blank. You can slide adjacent tiles into the blank. The goal is to reach a desired configuration. Search algorithms find the shortest sequence of moves.
Two Minute Drill
- Problem solving in AI involves finding a sequence of actions from start to goal.
- Key elements: state space, initial state, goal state, actions, path cost.
- Search algorithms: uninformed (BFS, DFS) and informed (A*, greedy).
- Heuristics guide search and make it more efficient.
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