Uncertainty in Artificial Intelligence
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Expected Utility Networks
Pierfrancesco La Mura, Yoav Shoham
Abstract:
We introduce a new class of graphical representations, expected utility networks (EUNs), and discuss some of its properties and potential applications to artificial intelligence and economic theory. In EUNs not only probabilities, but also utilities enjoy a modular representation. EUNs are undirected graphs with two types of arc, representing probability and utility dependencies respectively. The representation of utilities is based on a novel notion of conditional utility independence, which we introduce and discuss in the context of other existing proposals. Just as probabilistic inference involves the computation of conditional probabilities, strategic inference involves the computation of conditional expected utilities for alternative plans of action. We define a new notion of conditional expected utility (EU) independence, and show that in EUNs node separation with respect to the probability and utility subgraphs implies conditional EU independence.
Keywords: probability, utility, conditional, independence, expected utility, modular
Pages: 366-373
PS Link: http://www.stanford.edu/~plamura/lamurap.ps
PDF Link: /papers/99/p366-la_mura.pdf
BibTex:
@INPROCEEDINGS{La Mura99,
AUTHOR = "Pierfrancesco La Mura and Yoav Shoham",
TITLE = "Expected Utility Networks",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "366--373"
}


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