Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inferring Strategies from Limited Reconnaissance in Real-time Strategy
  Games   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.

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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio