A Bayesian Model for Opening Prediction in RTS Games with Application to StarCraft

Abstract : This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft1. This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game logs), labeled with openings. We present a semi- supervised method of labeling replays with the expectation- maximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.
Type de document :
Communication dans un congrès
Computational Intelligence and Games, Aug 2011, Seoul, South Korea. pp.000, 2011
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00607277
Contributeur : Gabriel Synnaeve <>
Soumis le : vendredi 8 juillet 2011 - 13:55:44
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02
Document(s) archivé(s) le : lundi 12 novembre 2012 - 10:30:37

Fichier

OpeningPrediction.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00607277, version 1

Collections

Citation

Gabriel Synnaeve, Pierre Bessiere. A Bayesian Model for Opening Prediction in RTS Games with Application to StarCraft. Computational Intelligence and Games, Aug 2011, Seoul, South Korea. pp.000, 2011. 〈hal-00607277〉

Partager

Métriques

Consultations de la notice

759

Téléchargements de fichiers

969