Abstract
Computer Daihinmin involves playing Daihinmin, a popular card game in Japan, by using a player program. Because strong player programs of Computer Daihinmin use machine-learning techniques, such as the Monte Carlo method, predicting the program’s behavior is difficult. In this study, we extract the features of the player program through decision tree analysis. The features of programs are extracted by generating decision trees based on three types of viewpoints. To show the validity of our method, computer experiments were conducted. We applied our method to three programs with relatively obvious behaviors, and we confirmed that the extracted features were correct by observing real behaviors of the programs.
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Konishi, M., Okubo, S., Nishino, T., Wakatsuki, M. (2018). Decision Tree Analysis in Game Informatics. In: Lee, R. (eds) Applied Computing & Information Technology. ACIT 2017. Studies in Computational Intelligence, vol 727. Springer, Cham. https://doi.org/10.1007/978-3-319-64051-8_2
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DOI: https://doi.org/10.1007/978-3-319-64051-8_2
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