On evidential reasoning in a hierarchy of hypotheses
Artificial Intelligence
A pattern classification approach to evaluation function learning
Artificial Intelligence
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
Decision-Theoretic Control of Reasoning: General Theory and an
Decision-Theoretic Control of Reasoning: General Theory and an
Representing and reasoning with probabilistic knowledge
Representing and reasoning with probabilistic knowledge
Constructor: a system for the induction of probabilistic models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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In this paper, we consider one aspect of the problem of applying decision theory to the design of agents that learn how to make decisions under uncertainty. This aspect concerns how an agent can estimate probabilities for the possible states of the world, given that it only makes limited observations before committing to a decision. We show that the naive application of statistical tools can be improved upon if the agent can determine which of his observations are truly relevant to the estimation problem at hand. We give a framework in which such determinations can be made, and define an estimation procedure to use them. Our framework also suggests several extensions, which show how additional knowledge can be used to improve the estimation procedure still further.