Uncertainty in Artificial Intelligence
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Probability Estimation in Face of Irrelevant Information
Adam Grove, Daphne Koller
Abstract:
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 tile estimation procedure still further.
Keywords:
Pages: 127-134
PS Link:
PDF Link: /papers/91/p127-grove.pdf
BibTex:
@INPROCEEDINGS{Grove91,
AUTHOR = "Adam Grove and Daphne Koller",
TITLE = "Probability Estimation in Face of Irrelevant Information",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "127--134"
}


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