Uncertainty in Artificial Intelligence
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Efficient algorithm for estimation of qualitative expected utility in possibilistic case-based reasoning
Jakub Brzostowski, Ryszard Kowalczyk
Abstract:
We propose an efficient algorithm for estimation of possibility based qualitative expected utility. It is useful for decision making mechanisms where each possible decision is assigned a multi-attribute possibility distribution. The computational complexity of ordinary methods calculating the expected utility based on discretization is growing exponentially with the number of attributes, and may become infeasible with a high number of these attributes. We present series of theorems and lemmas proving the correctness of our algorithm that exibits a linear computational complexity. Our algorithm has been applied in the context of selecting the most prospective partners in multi-party multi-attribute negotiation, and can also be used in making decisions about potential offers during the negotiation as other similar problems.
Keywords:
Pages: 69-76
PS Link:
PDF Link: /papers/05/p69-brzostowski.pdf
BibTex:
@INPROCEEDINGS{Brzostowski05,
AUTHOR = "Jakub Brzostowski and Ryszard Kowalczyk",
TITLE = "Efficient algorithm for estimation of qualitative expected utility in possibilistic case-based reasoning",
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 = "69--76"
}


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