Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2014

Abstract

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.

Keywords

Algorithms, Data mining, Critical moment, Pattern mining, Prefix spans, Sequence mining, Tetris game, Turning points

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2014 SIAM International Conference on Data Mining: April 24-26, Philadelphia, PA

First Page

956

Last Page

964

ISBN

9781611973440

Identifier

10.1137/1.9781611973440.109

Publisher

SIAM

City or Country

Philadelphia, PA

Copyright Owner and License

LARC

Additional URL

https://doi.org/10.1137/1.9781611973440.109

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