On Finding the Point Where There Is No Return: Turning Point Mining on Game Data

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.

[1]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[2]  Kevin Crowley,et al.  Flexible Strategy Use in Young Children's Tic-Tac-Toe , 1993, Cogn. Sci..

[3]  Bruno Scherrer,et al.  Building Controllers for Tetris , 2009, J. Int. Comput. Games Assoc..

[4]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  Niko Bohm,et al.  An Evolutionary Approach to Tetris , 2005 .

[6]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[7]  Sangkyum Kim,et al.  NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification , 2010, ECML/PKDD.

[8]  Umeshwar Dayal,et al.  FreeSpan: frequent pattern-projected sequential pattern mining , 2000, KDD '00.

[9]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[10]  Mohammed J. Zaki,et al.  Lazy Associative Classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[11]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[12]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[13]  Mohammed J. Zaki,et al.  Mining features for sequence classification , 1999, KDD '99.

[14]  Erik D. Demaine,et al.  Tetris is hard, even to approximate , 2002, Int. J. Comput. Geom. Appl..

[15]  Jian Pei,et al.  A brief survey on sequence classification , 2010, SKDD.