On finding the point where there is no return: Turning point mining on game data

• Main Authors: GONG, Wei, LIM, Ee Peng, ZHU, Feida, PALAKORN, Achananuparp, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Algorithms; Data mining; Critical moment; Pattern mining; Prefix spans; Sequence mining; Tetris game; Turning points; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1978; https://ink.library.smu.edu.sg/context/sis_research/article/2977/viewcontent/SDM_14_ZhuFD.pdf
• Institution: Singapore Management University

Gaming expertise is usually accumulated through playing or watching many game instances, and identifying critical moments in these game instances called turning points. Turning point rules (shorten as TPRs) are game patterns that almost always lead to some irreversible outcomes. In this paper, we formulate the notion of irreversible outcome property which can be combined with pattern mining so as to automatically extract TPRs from any given game datasets. We specifically extend the well-known PrefixSpan sequence mining algorithm by incorporating the irreversible outcome property. To show the usefulness of TPRs, we apply them to Tetris, a popular game. We mine TPRs from Tetris games and generate challenging game sequences so as to help training an intelligent Tetris algorithm. Our experiment results show that 1) TPRs can be found from historical game data automatically with reasonable scalability, 2) our TPRs are able to help Tetris algorithm perform better when it is trained with challenging game sequences.