The minimax theorem states (source: Wikipedia)įor every two-person, zero-sum game with finitely many strategies, there exists a value V and a mixed strategy for each player, such that: (a) Given player 2’s strategy, the best payoff possible for player 1 is V, and It is a variant of minimax and an alternative to the alpha-beta pruning algorithm.
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The name MTD(f) is an abbreviation for MTD(n,f) (Memory-enhanced Test Driver with node n and value f). It was developed in 1994 by Aske Plaat, Jonathan Schaeffer, Wim Pijls, and Arie de Bruin.
MTD(f), is a higly optimized minimax algorithm. MTD(f) = minimax + horizon + alpha-beta pruning + null-window + iterative deepening + transposition table This time I went a little further and improved the minimax algorithm by adding a “horizon”, “alpha-beta pruning”, a “null-window”, “iterative deepening”, and a “transposition table” turning the minimax into a “M emory-enhanced T est D river” also known as: MTD(f). When implementing some AI for TicTacToe I chose the basic “minimax” algorithm without any optimization. This time I implemented “Connect Four” (also known as “Four in a Row” and “Four in a Line”). Last month, I wrote a post about implementing TicTacToe in Python. My favorite three games to implement when learning a new programming are: TicTacToe, Connect Four, and Minesweeper.
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