Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
The problem with minimax algorithm search is that the number of game states it has to examine is exponential in the number of moves.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
The minimax algorithm computes the minimax decision from the current state. It is used as a searching technique in game problems. The minimax algorithm performs a complete depth-first exploration of the game-tree.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
As for every move a player makes in the game of chess, the average branching factor is 35, i.e., the opponent can make 35 different moves.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
The initial state, which includes the board position and identifies the player to move. A successor function, which returns a list of (move, state) pairs, each indicating a legal move and the resulting state.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
Players must choose their strategies simultaneously, neither knowing what the other player is going to do.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
Characteristics of Game Playing: - 1. There is always an "unpredictable" opponent - Opponent introduces uncertainty.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
How relevant the game theory is to mathematics, computer science and economics.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
There were two reasons that games appeared to be a good domain in which to explore machine intelligence-They provide a structured task in which it is very easy to measure success or failure.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
Game theory does not prescribe a way or say how to play a game. Game theory is a set of ideas and techniques for analyzing conflict situations between two or more parties.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
A game has at least two players. Solitaire is not considered a game by game theory. The term "solitaire" is used for single-player games of concentration.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
Applications of game theory are wide-ranging. Von Neunmann and Morgenstern indicated the utility of game theory by linking with ecomomic behavior.
Adversarial search - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit I(e): Adversarial search
In adversarial search problem environment is multiagent, competitive where in the agent's goals are in coflict. Adversarial search problems are commonly known as games.