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2006
ACM

From statistical knowledge bases to degrees of belief: an overview

12 years 5 months ago
From statistical knowledge bases to degrees of belief: an overview
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 expectedutility paradigm of decision theory to guide its actions, she 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. This paper provides a brief overview of one approach for inducing degrees of belief from very rich knowledge bases that can include information about particular individuals, statistical correlations, physical laws, and default rules. The approach is called the random-worlds method. The method is based on the principle of indifference: it treats all of the worlds the agent considers possible as being equally likely. It is able to integrate qualitative default reasoning with quantit...
Joseph Y. Halpern
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2006
Where PODS
Authors Joseph Y. Halpern
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