![]() Time Management for Monte-Carlo Tree Search in Go. Artificial Intelligence: a modern approach, Ch.Minimax, alpha-beta pruning, heuristic evaluation functions Prerequisite Knowledge and Associated Readings Having 254 fair initial game states allows for variety of game play. ![]() Kalah), and game play may be modified with the caveat that given fair board positions would no longer be expected to be fair under a different rule set. There are many variants of Mancala (a.k.a. Students must have intermediate undergraduate programming skill and become familiar with the concepts of the relevant readings below. In this unusual first release of research results via an assignment, we fix an unfair, common, accessible game to give it fresh life for use in the classroom.Īvailable in Java, Python, and Ludii. In Mancala and some other games commonly used in AI education, a first-player advantage obscures evaluation of relative AI player strength. In providing 254 fair initial board positions, we enable better evaluation of real-time, game-playing AI. Undergraduate or graduate course with a focus on AI programming Game-tree search, minimax, alpha-beta pruning, heuristic evaluation, time management, bounded rationality Gettysburg College Department of Computer ScienceįairKalah: fair Mancala competition - Students experientially learn about alpha-beta pruning, heuristic evaluation, and time management in real-time, fair Mancala competition. FairKalah: Fair Mancala Competition FairKalah: Fair Mancala Competition ![]()
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