From Statistical Knowledge Bases to Degrees of Belief (1996)
| Venue: | Artificial Intelligence |
| Citations: | 104 - 8 self |
BibTeX
@ARTICLE{Bacchus96fromstatistical,
author = {Fahiem Bacchus and Computer Science Dept and Adam J. Grove and Joseph Y. Halpern and Daphne Koller},
title = {From Statistical Knowledge Bases to Degrees of Belief},
journal = {Artificial Intelligence},
year = {1996},
volume = {87},
pages = {75--143}
}
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Abstract
An intelligent agent will often be uncertain about various properties of its environment, and when acting in that environment it will frequently need to quantify its uncertainty. For example, if the agent wishes to employ the expected-utility paradigm of decision theory to guide its actions, it will need to assign degrees of belief (subjective probabilities) to various assertions. Of course, these degrees of belief should not be arbitrary, but rather should be based on the information available to the agent. For example, consider a doctor who must decide upon the treatment for a particular patient. To do so she would have to evaluate, for various diseases, a degree of belief in the patient having that disease. In making that evaluation, the doctor would want to use all of her knowledge about the patient, about statistical correlations between symptoms and diseases, about physical laws, about default rules, etc. This paper describes one approach for inducing degrees of belief from very ...
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