We propose a visualization approach for analyzing players' action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and KeyGraph. CMDS is for discovering clusters of players who behave similarly. KeyGraph is for interpreting action behaviors of players in a cluster of interest. In order to reduce the dimension of matrices used in computation of the CMDS input, we exploit a time-series reduction technique recently proposed by us. Our visualization approach is evaluated using log of an online game where three-player types according to Bartle's taxonomy are found, that is, achievers, explorers, and socializers.
Game playing has provided an interesting framework for developing and testing artificial intelligence (AI) algorithms, with well-known examples such as Go and Chess. Since the first chess programs were written, there has been steady progress in the level of play to the point where current systems can challenge top-class human players. Recently, academia groups have created competitions to evaluate and compare AI methods, such as Ms Pac-Man versus Ghost Team and Student StarCraft AI Tournament. Those tournaments define their rules and goals based on the game they selected, according to which intelligent techniques and technologies are developed. We focus on a genre called fighting game. Examples of fighting games are Street Fighter, Tekken, and Mortal Kombat. To select the winner in the fighting game is simple: the winner is the character with less damage or the last one standing. In this paper we introduce a brand new competition 1 based on a 2D fighting game written in Java.
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