Uncertainty in Artificial Intelligence
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Local Utility Elicitation in GAI Models
Darius Braziunas, Craig Boutilier
Abstract:
Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.
Keywords:
Pages: 42-49
PS Link:
PDF Link: /papers/05/p42-braziunas.pdf
BibTex:
@INPROCEEDINGS{Braziunas05,
AUTHOR = "Darius Braziunas and Craig Boutilier",
TITLE = "Local Utility Elicitation in GAI Models",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "42--49"
}


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