Abstract : This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player's orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strategic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components
https://hal.archives-ouvertes.fr/hal-00752893
Contributor : Gabriel Synnaeve
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Submitted on : Friday, November 16, 2012 - 3:51:15 PM
Last modification on : Friday, October 12, 2018 - 1:18:12 AM
Document(s) archivé(s) le : Saturday, December 17, 2016 - 11:17:59 AM
Gabriel Synnaeve, Pierre Bessiere. A Dataset for StarCraft AI & an Example of Armies Clustering. Artificial Intelligence in Adversarial Real-Time Games 2012, Oct 2012, Palo Alto, United States. pp 25-30. ⟨hal-00752893⟩
