A Pattern Mining Approach to Study Strategy Balance in RTS Games
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Tan_A pattern mining approach.pdf
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Author(s)
Bosc, Guillaume
Boulicaut, Jean-Francois
Raissi, Chedy
Kaytoue, Mehdi
Tan, Philip
Date Issued
June 2017
Journal
IEEE Transactions on Computational Intelligence and AI in Games
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bosc, Guillaume; Tan, Philip; Boulicaut, Jean-Francois; Raissi, Chedy and Kaytoue, Mehdi. “A Pattern Mining Approach to Study Strategy Balance in RTS Games.” IEEE Transactions on Computational Intelligence and AI in Games 9, no. 2 (June 2017): 123–132 © 2017 Institute of Electrical and Electronics Engineers (IEEE)
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Author's final manuscript
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 developments 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 paper, we consider real-time strategy (RTS) games 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 (KDD) process. We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/TCIAIG.2015.2511819
