A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft

Abstract : The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.
Type de document :
Communication dans un congrès
AAAI. Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2011), Oct 2011, Palo Alto, United States. pp.79--84, 2011, Proceedings of AIIDE
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00641323
Contributeur : Gabriel Synnaeve <>
Soumis le : mercredi 16 novembre 2011 - 00:08:07
Dernière modification le : vendredi 12 octobre 2018 - 01:17:58
Document(s) archivé(s) le : vendredi 16 novembre 2012 - 11:00:54

Fichiers

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

Identifiants

  • HAL Id : hal-00641323, version 1
  • ARXIV : 1111.3735

Collections

Citation

Gabriel Synnaeve, Pierre Bessière. A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft. AAAI. Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2011), Oct 2011, Palo Alto, United States. pp.79--84, 2011, Proceedings of AIIDE. 〈hal-00641323〉

Partager

Métriques

Consultations de la notice

620

Téléchargements de fichiers

242