Special Tactics: a Bayesian Approach to Tactical Decision-making

Abstract : We describe a generative Bayesian model of tactical attacks in strategy games, which can be used both to predict attacks and to take tactical decisions. This model is designed to easily integrate and merge information from other (probabilistic) estimations and heuristics. In particular, it handles uncertainty in enemy units' positions as well as their probable tech tree. We claim that learning, being it supervised or through reinforcement, adapts to skewed data sources. We evaluated our approach on StarCraft 1 : the parameters are learned on a new (freely available) dataset of game states, deterministically re-created from replays, and the whole model is evaluated for prediction in realistic conditions. It is also the tactical decision-making component of our StarCraft AI competition bot.
Type de document :
Communication dans un congrès
IEEE. Proceedings of the IEEE Conference on Computational Intelligence and Games, Sep 2012, Granada, Spain. IEEE, pp.978-1-4673-1194-6/12/ 409-416, 2012, Proceedings of CIG


https://hal.archives-ouvertes.fr/hal-00752841
Contributeur : Gabriel Synnaeve <>
Soumis le : vendredi 16 novembre 2012 - 15:20:15
Dernière modification le : mercredi 28 septembre 2016 - 16:19:48

Fichier

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

Identifiants

  • HAL Id : hal-00752841, version 1

Collections

Citation

Gabriel Synnaeve, Pierre Bessière. Special Tactics: a Bayesian Approach to Tactical Decision-making. IEEE. Proceedings of the IEEE Conference on Computational Intelligence and Games, Sep 2012, Granada, Spain. IEEE, pp.978-1-4673-1194-6/12/ 409-416, 2012, Proceedings of CIG. <hal-00752841>

Exporter

Partager

Métriques

Consultations de
la notice

225

Téléchargements du document

159