Artificial Intelligence - Adversarial Search & Minimax

These flashcards cover key concepts related to adversarial search and the minimax algorithm in artificial intelligence.

Adversarial Search

A search technique used in environments where other agents may act against the player's interest.

Minimax Algorithm

An algorithm used in decision-making and game theory that provides a strategy for minimizing the possible loss while maximizing the potential gain.

Perfect Information Game

A game in which all players have complete knowledge of the game state at all times.

Zero-Sum Game

A type of game where one player's gain is exactly equal to another player's loss.

Game Tree

A tree representation of all possible moves in a game, leading from an initial state to terminal states.

Terminal State

A state in a game where the game has finished and no further moves are possible.

Utility Function

A function that assigns a numeric value to a player's position in a game, representing the desirability of that position.

Alpha-Beta Pruning

An optimization technique for the minimax algorithm that reduces the number of nodes evaluated in the search tree.

Monte Carlo Tree Search

A method used for making decisions in artificial intelligence game-playing that relies on random sampling of game states.

Heuristic Evaluation Function

A function used to estimate the value of a game position, providing a measure of how favorable a position is for a player.