Uncertainty in Artificial Intelligence
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Graphical Models for Preference and Utility
Fahiem Bacchus, Adam Grove
Abstract:
Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multi-attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive independence, which we show always has a perfect representation as separation in an undirected graph (a Markov network). Conditional additive independencies entail a particular functional for the utility function that is analogous to a product decomposition of a probability function, and confers analogous benefits. This functional form has been utilized in the Bayesian network and influence diagram literature, but generally without an explanation in terms of independence. The functional form yields a decomposition of the utility function that can greatly speed up expected utility calculations, particularly when the utility graph has a similar topology to the probabilistic network being used.
Keywords:
Pages: 3-10
PS Link:
PDF Link: /papers/95/p3-bacchus.pdf
BibTex:
@INPROCEEDINGS{Bacchus95,
AUTHOR = "Fahiem Bacchus and Adam Grove",
TITLE = "Graphical Models for Preference and Utility",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "3--10"
}


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