Uncertainty in Artificial Intelligence
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Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games
Jesse Hostetler, Ethan Dereszynski, Thomas Dietterich, Alan Fern
Abstract:
In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent's disposition is limited to what can be observed through scouting. Information is costly, since units dedicated to scouting are unavailable for other purposes, and the enemy will resist scouting attempts. It is important to infer as much as possible about the opponent's current and future strategy from the available observations. We present a dynamic Bayes net model of strategies in the RTS game Starcraft that combines a generative model of how strategies relate to observable quantities with a principled framework for incorporating evidence gained via scouting. We demonstrate the model's ability to infer unobserved aspects of the game from realistic observations.
Keywords:
Pages: 367-376
PS Link:
PDF Link: /papers/12/p367-hostetler.pdf
BibTex:
@INPROCEEDINGS{Hostetler12,
AUTHOR = "Jesse Hostetler and Ethan Dereszynski and Thomas Dietterich and Alan Fern",
TITLE = "Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "367--376"
}


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