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Mining Balanced Sequential Patterns in RTS Games 1

Identifieur interne : 000993 ( Main/Exploration ); précédent : 000992; suivant : 000994

Mining Balanced Sequential Patterns in RTS Games 1

Auteurs : Guillaume Bosc [France] ; Mehdi Kaytoue [France] ; Chedy Raïssi [France] ; Jean-François Boulicaut [France] ; Philip Tan [États-Unis]

Source :

RBID : Hal:hal-01100933

Abstract

The video game industry has grown enormously over the last twenty years, bringing new challenges to the artificial intelli-gence and data analysis communities. We tackle here the problem of automatic discovery of strategies in real-time strategy games through pattern mining. Such patterns are the basic units for many tasks such as automated agent design, but also to build tools for the profession-ally played video games in the electronic sports scene. Our formal-ization relies on a sequential pattern mining approach and a novel measure, the balance measure, telling how a strategy is likely to win. We experiment our methodology on a real-time strategy game that is professionally played in the electronic sport community.

Url:
DOI: 10.3233/978-1-61499-419-0-975


Affiliations:


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Le document en format XML

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<div type="abstract" xml:lang="en">The video game industry has grown enormously over the last twenty years, bringing new challenges to the artificial intelli-gence and data analysis communities. We tackle here the problem of automatic discovery of strategies in real-time strategy games through pattern mining. Such patterns are the basic units for many tasks such as automated agent design, but also to build tools for the profession-ally played video games in the electronic sports scene. Our formal-ization relies on a sequential pattern mining approach and a novel measure, the balance measure, telling how a strategy is likely to win. We experiment our methodology on a real-time strategy game that is professionally played in the electronic sport community.</div>
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