N-Tuple Network Search in Othello Using Genetic Algorithms

Hiroto Kuramitsu (), Kaiyu Suzuki and Tomofumi Matsuzawa
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Hiroto Kuramitsu: Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan
Kaiyu Suzuki: Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan
Tomofumi Matsuzawa: Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan

Games, 2025, vol. 16, issue 1, 1-11

Abstract: As one of the strongest Othello agents, Edax employs an n-tuple network to evaluate the board, with points of interest represented as tuples. However, this network maintains a constant shape throughout the game, whereas the points of interest in Othello vary with respect to game’s progress. The present study was conducted to optimize the shape of the n-tuple network using a genetic algorithm to maximize final score prediction accuracy for a certain number of moves. We selected shapes for 18-, 22-, 26-, 30-, 34-, 38-, 42-, and 46-move configurations, and constructed an agent that appropriately shapes an n-tuple network depending on the progress of the game. Consequently, agents using the n-tuple network developed in this study exhibited a winning rate of 75%. This method is independent of game characteristics and can optimize the shape of larger (or smaller) N-tuple networks.

Keywords: Othello; genetic algorithm; n-tuple network (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2025
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