A Pattern Mining Approach to Study Strategy Balance in RTS Games

Guillaume Bosc 1 Philip Tan 2 Jean-François Boulicaut 1 Chedy Raïssi 3 Mehdi Kaytoue 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 GameLab MIT
MIT - Massachusetts Institute of Technology
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Whereas purest strategic games such as Go and Chess seem timeless, the lifetime of a video game is short, influenced by popular culture, trends, boredom and technological innovations. Even the important budget and de- velopments allocated by editors cannot guarantee a timeless success. Instead, novelties and corrections are proposed to extend an inevitably bounded lifetime. Novelties can unexpectedly break the balance of a game, as players can discover unbalanced strategies that developers did not take into account. In the new context of electronic sports, an important challenge is to be able to detect game balance issues. In this article, we consider real time strategy games (RTS) and present an efficient pattern mining algorithm as a basic tool for game balance designers that enables one to search for unbalanced strategies in historical data through a Knowledge Discovery in Databases process (KDD). We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport.
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Guillaume Bosc, Philip Tan, Jean-François Boulicaut, Chedy Raïssi, Mehdi Kaytoue. A Pattern Mining Approach to Study Strategy Balance in RTS Games. IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2017, 9 (2), pp.123-132. ⟨10.1109/TCIAIG.2015.2511819⟩. ⟨hal-01252728⟩

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