Game Playing:
Game playing in AI involves designing algorithms that can play games (e.g., chess, tic-tac-toe) against human players or other AI systems.
Thank you for reading this post, don't forget to subscribe!- These games typically involve two players (adversaries) who take turns, and the goal is to find the best move to maximize the chances of winning.
- The most common search technique in game playing is Minimax search procedure.
Key Concepts:
- Adversarial Nature: The opponent’s moves are unpredictable, so the algorithm must plan for all possible moves.
- Decision-Making: The AI must evaluate the current state of the game and choose the best action to achieve a winning outcome.
Adversarial Search Techniques:
Adversarial search techniques are algorithms used in two-player games where the players have opposing goals.
- These techniques explore the possible moves of both players to determine the best strategy.
Key Concepts:
- Search Tree: Represents all possible moves and countermoves in the game.
- Mini-Max Algorithm: A foundational technique for adversarial search.
- Alpha-Beta Pruning: An optimization of the Mini-Max algorithm to reduce the number of nodes evaluated.
1.) Mini-Max Search
Mini-Max is an adversarial search algorithm used in two-player games to determine the optimal move for a player, assuming the opponent also plays optimally.
How it Works:
The algorithm alternates between two players:
- Maximizing Player: Tries to maximize its own score.
- Minimizing Player: Tries to minimize the opponent’s score.
It explores the game tree to evaluate all possible moves and countermoves, assigning a value to each terminal state (win, lose, or draw).
Example:
- In tic-tac-toe, Mini-Max evaluates all possible moves to ensure a win or at least a draw.
2.) Alpha-Beta Pruning:
Alpha-Beta Pruning is an optimization of the Mini-Max algorithm that reduces the number of nodes evaluated in the search tree by pruning branches that cannot influence the final decision.
How it Works:
- It uses two values, alpha (the best value for the maximizing player) and beta (the best value for the minimizing player), to eliminate unnecessary branches.
- If a branch is found to be worse than the current best option, it is pruned (not explored further).
Example:
- In chess, Alpha-Beta Pruning avoids evaluating moves that are clearly worse than previously explored moves.