Learning evaluation functions of Shogi positions from different sets of games

Authors:
Kosuke Inagaki;Ryohei Nakano
Affiliations:
Nagoya Institute of Technology, Nagoya, Japan;Nagoya Institute of Technology, Nagoya, Japan
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

This paper addresses learning of a reasonably accurate evaluation function of Shogi (Japanese Chess) positions through learning from records of games. Accurate evaluation of a Shogi position is indispensable for a computer Shogi program. A Shogi position is projected into several semantic features characterizing the position. Using such features as input, we employ reinforcement learning with a multi-layer perceptron as a nonlinear function approximator. We prepare two completely different sets of games: games played by computer Shogi programs and games played by professional Shogi players. Then we built two evaluation functions by separate learning based on two different sets of games, and compared the results to find several interesting tendencies.