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Topic 9 of 19
09
Game-playing in AI
Game playing in AI refers to designing systems that can play games intelligently against humans or other machines. AI uses search algorithms and decision-making strategies to determine...
📘 Problem Solving by Searching: Game Playing
1. What is Game Playing in AI?
Game playing in AI refers to designing systems that can play games intelligently against humans or other machines. AI uses search algorithms and decision-making strategies to determine the best move in a game.
AI treats the game as a search problem, where each move leads to a new state in the game tree.
2. Types of Games in AI
| Game Type | Example | Features |
|---|---|---|
| Deterministic | Chess, Checkers | No chance elements, fully predictable |
| Stochastic | Backgammon | Involves randomness (dice, chance) |
| Turn-based | Tic-Tac-Toe | Players alternate turns |
| Simultaneous | Rock-Paper-Scissors | Players act at the same time |
| Zero-sum | Most board games | One player’s gain = another’s loss |
| Non-zero-sum | Trading games | Players can benefit together |
3. Game as a Search Problem
Each game is modeled as a search tree:
- Initial State: The current board or game setup.
- Actions: Possible legal moves from the current state.
- Successor Function: Resulting state after an action.
- Terminal Test: Checks if the game is over.
- Utility Function: Assigns a numeric value (score) to terminal states (e.g., +1 win, –1 loss, 0 draw).
4. Key Algorithms for Game Playing
| Algorithm | Description | Used In |
|---|---|---|
| Minimax | Assumes opponent plays optimally; maximizes AI’s score while minimizing opponent's | Chess, Tic-Tac-Toe |
| Alpha-Beta Pruning | Optimized Minimax; prunes unnecessary branches to speed up search | All deterministic games |
| Expectiminimax | Extension of Minimax for stochastic games; accounts for chance nodes (like dice) | Backgammon |
| Monte Carlo Tree Search (MCTS) | Random simulations used to explore the most promising moves | Go, Poker, complex games |
5. Minimax Overview (Recap)
- Maximizer: AI player
- Minimizer: Opponent
- Evaluates all possible outcomes and chooses the best move assuming the opponent also plays perfectly.
6. Real-World Examples
| Game | AI System |
|---|---|
| Chess | Deep Blue (beat human world champion) |
| Go | AlphaGo (used neural networks + MCTS) |
| Poker | Libratus (used game theory + simulation) |
| Tic-Tac-Toe | Simple Minimax implementation |
✅ Summary
| Concept | Explanation |
|---|---|
| Game Playing | AI agents playing games using search and decision-making |
| Search Tree | Represents all possible game states |
| Minimax | Used in deterministic, turn-based, zero-sum games |
| Alpha-Beta Pruning | Makes Minimax more efficient |
| Utility Function | Scores outcomes of games |
| Use Cases | Games, simulations, decision-making, strategy AI |
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