A Decision Tree Analysis of a Multi-Player Card Game With Imperfect Information

Abstract

This article describes how 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 article, the authors 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 their method, computer experiments were conducted. The authors applied their method to three programs with relatively obvious behaviors, and they confirmed that the extracted features were correct by observing real behaviors of the programs.