Artificial Intelligence : Unit - 2 Part - 11 : Game Playing – Adversarial Search

 

UNIT - II

Games in Artificial Intelligence

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Part A: Introduction

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✅ What is a Game in AI?

In Artificial Intelligence, a game is a competitive scenario involving two or more players, where each player makes moves based on a set of rules, with the goal of winning or achieving a higher score than the other(s).

Games provide an excellent way to study decision making, strategy, and problem-solving in adversarial environments (i.e., environments where others compete against you).

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✅ Why Are Games Important in AI?

• Games offer a controlled environment to develop and test intelligent agents.
• Help in building AI systems that can plan ahead, predict outcomes, and make strategic decisions.
• Many AI algorithms (like Minimax, A*) are tested and applied first in games.

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Part B: Types of Games

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Type of Game	Description	Example
Deterministic	No chance or randomness involved	Chess, Checkers
Stochastic	Involves randomness or probability	Ludo, Dice games
Perfect Information	All players can see everything	Chess, Tic-Tac-Toe
Imperfect Information	Some information is hidden from players	Poker, Battleship
Single Player Game	Only one player solving a puzzle	Sudoku, Solitaire
Two Player Game	Two players compete directly	Chess, Connect Four
Zero-Sum Game	One player’s win is another player’s loss	Tic-Tac-Toe, Chess
Non-Zero Sum Game	Multiple players can benefit together	Business simulation games

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Part C: Game Components in AI

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To represent games in AI, we model them as search problems using the following components:

Component	Description
Initial State	The starting point of the game (e.g., empty board in Tic-Tac-Toe)
Players	The agents who take turns playing the game
Actions	The legal moves available to a player at a certain state
Transition Model	Rules that define the result of an action
Terminal Test	Checks if the game has ended (win, lose, draw)
Utility Function	Assigns values to terminal states (+1 win, 0 draw, -1 loss)

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✅ Example: Tic-Tac-Toe

Game Element	Description
Initial State	3x3 empty grid
Players	MAX (X) and MIN (O)
Actions	Place X or O in any empty square
Terminal Test	Board is full or one player has won
Utility Function	+1 (win), 0 (draw), -1 (loss)

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Part D: Game Trees

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A game tree is a tree-like structure used to represent all possible moves in a game, where:

• Nodes represent game states
• Edges represent player moves
• MAX and MIN levels alternate for each player's turn

✅ Used in:

• Minimax Algorithm
• Alpha-Beta Pruning

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Part E: Game Playing as Search

Games are solved using search strategies where:

• The AI explores possible future moves
• Evaluates the outcomes using heuristics
• Selects the best action to move closer to victory

🧠 Examples of Search Techniques in Games:

• Minimax Algorithm – for optimal decisions in 2-player zero-sum games
• Alpha-Beta Pruning – speeds up Minimax by skipping unneeded nodes
• Monte Carlo Tree Search (MCTS) – used in complex games like Go

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✅ Real-World Applications of Game AI

Application Area	Use Case
Game Development	AI players in Chess, Ludo, Strategy games
Education	Building logic and decision-making skills through game AI
Simulation & Planning	AI simulation in military or business decision training
Entertainment AI	Non-player character (NPC) behavior in games like PUBG, FIFA

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Part F: Advantages and Challenges of Game AI

Advantages	Challenges
✅ Helps understand decision-making	❌ Game trees can grow huge (combinatorial explosion)
✅ Encourages strategic and logical AI	❌ Real-time games need fast decisions
✅ Builds intelligent and adaptive agents	❌ Imperfect information and chance make predictions harder

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📝 Summary

• Games in AI are environments for adversarial search where agents compete with one another.
• Games are modeled as search problems with components like states, actions, players, and utility functions.
• AI uses algorithms like Minimax and Alpha-Beta Pruning to make smart decisions.
• Game playing is a core area in AI, helping build agents that can think, plan, and adapt.

"Games are not just fun—they're a battlefield for AI to learn, adapt, and win!"