Game Playing and Adversarial Search

Mon, 01 Jan 0001 00:00:00 +0000

Game Playing and Adversarial Search #

Game-playing agents search in environments where other agents influence the outcome. In competitive games, a good move must account not only for what the agent wants to achieve, but also for the strongest response an opponent can make.

Searching to play games
Minimax Algorithm
Alpha-Beta Pruning
Monte Carlo Tree Search
Stochastic Games

Learning Objectives #

formulate a game as an adversarial search problem
construct a game tree with alternating MAX and MIN turns
design and interpret a static evaluation function
back up values using Minimax
apply Alpha-Beta Pruning and identify cut-offs
explain the four phases of Monte Carlo Tree Search
calculate an MCTS selection score using UCB1
evaluate chance outcomes in a stochastic game

Big Picture #

flowchart TD
 A["Game state"] --> B["Generate legal moves"]
 B --> C["Build game tree"]
 C --> D["Evaluate leaves"]
 D --> E["Choose robust action"]
 E --> A

 style A fill:#E1F5FE
 style B fill:#FFF9C4
 style C fill:#EDE7F6
 style D fill:#FFF9C4
 style E fill:#C8E6C9

1. Normal Search and Adversarial Search #

In normal search, the agent controls its own actions and the transition model determines what happens next. In adversarial search, another agent chooses actions that may reduce the first agent’s utility.

Game Playing on Arshad Siddiqui

Game Playing and Adversarial Search

Game Playing and Adversarial Search #

Learning Objectives #

Big Picture #

1. Normal Search and Adversarial Search #