Uncertainty in Artificial Intelligence
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Utility-Based Abstraction and Categorization
Eric Horvitz, Adrian Klein
Abstract:
We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction.
Keywords:
Pages: 128-135
PS Link:
PDF Link: /papers/93/p128-horvitz.pdf
BibTex:
@INPROCEEDINGS{Horvitz93,
AUTHOR = "Eric Horvitz and Adrian Klein",
TITLE = "Utility-Based Abstraction and Categorization",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "128--135"
}


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