An analysis of play style of advanced mahjong players toward the implementation of strong AI player. Issue 2 (4th March 2017)

• Record Type: Journal Article
• Authors: Sato, Hiroshi; Shirakawa, Tomohiro; Hagihara, Akitoshi; Maeda, Kento
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 32:Issue 2(2017)
• Page Start: 195
• Page End: 205
• Publication Date: 2017-03-04
• Subjects: Game AI -- machine Learning -- Mahjong; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2015.1049267
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : The studies of artificial intelligence (AI) on the game with perfect information has been very much advanced to have an ability to compete top-rate human players. In contrast, it is still difficult for AI to seek the best strategy of the facing situation in the games with imperfect information. In this type of the games, it is usually more effective for a player to adopt the strategy which match the other players' strategies than to find an optimal strategy. In this study, we took mahjong as an example of complicated games with imperfect information. We propose a new method to classify opponents' strategies in mahjong by analyzing play records of mahjong called 'Haihu' statistically. We find that the advanced mahjong players' behaviors are classified into four patterns. We also validate out result by implementing simple AI programs.