Title: Comparison Training of Shogi Evaluation Functions with Self-Generated Training Positions and Moves
Abstract: Automated tuning of parameters in computer game playing is an important technique for building strong computer programs. Comparison training is a supervised learning method for tuning the parameters of an evaluation function. It has proven to be effective in the game of Chess and Shogi. The training method requires a large number of training positions and moves extracted from game records of human experts; however, the number of such game records is limited. In this paper, we propose a practical approach to create additional training data for comparison training by using the program itself. We investigate three methods for generating additional positions and moves. Then we evaluate them using a Shogi program. Experimental results show that the self-generated training data can improve the playing strength of the program.
Publication Year: 2014
Publication Date: 2014-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 3
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