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The statistics of state-spaces

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Abstract

The state-space model is a general, powerful, and elegant representation of problem solving. Nevertheless, state-spaces have rarely been used to model realistic environments because conventional state-spaces are inherently deterministic, while the world is not. This paper extends the model toprobabilistic state-spaces (PSS), which elegantly capture many of the world's complexities by regarding state values as generated by random variables. These PSS models, when combined with decision analytic techniques for knowledge elicitation and encoding, should yield realistic representations. The first investigation of states based on random variables derived the expected-outcome model of two-player games, which led to some powerful results about game evaluators. Most of the work done on expected-outcome was quite general, and should extend easily beyond game-trees to arbitrary state-spaces. The second potential application domain, strategic planning for robot controllers in automated assembly plants, is currently under investigation.

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Authors and Affiliations

  1. Department of Computer Science, University of Southern California, 90089-0782, Los Angeles, CA, USA

    Bruce Abramson

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  1. Bruce Abramson
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Additional information

This research was supported in part by NSF grants IST-8513989 and IRI-8910173 and by the University of Southern California Faculty Research Initiation Fund.

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Abramson, B. The statistics of state-spaces. Ann Math Artif Intell 2, 5–20 (1990). https://doi.org/10.1007/BF01530994

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