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UCP-networks: a directed graphical representation of conditional utilities

Authors:

Craig Boutilier,

Fahiem Bacchus,

Ronen I. BrafmanAuthors Info & Claims

UAI'01: Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence

Pages 56 - 64

Published: 02 August 2001 Publication History

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Abstract

We propose a directed graphical representation of utility functions, called UCP-networks, that combines aspects of two existing preference models: generalized additive models and CP-networks. The network decomposes a utility function into a number of additive factors, with the directionality of the arcs reflecting conditional dependence in the underlying (qualitative) preference ordering under a ceteris paribus interpretation. The CP-semantics ensures that computing optimization and dominance queries is very efficient. We also demonstrate the value of this representation in decision making. Finally, we describe an interactive elicitation procedure that takes advantage of the linear nature of the constraints on "tradeoff weights" imposed by a UCP-network.

References

[1]

Fahiem Bacchus and Adam Grove. Graphical models for preference and utility. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 3-10, Montreal, 1995.

Digital Library

Google Scholar

[2]

Fahiem Bacchus and Adam Grove. Utility independence in qualitative decision theory. In Proeeedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning, pages 542-552, Cambridge, 1996.

Google Scholar

[3]

Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, and David Poole. Reasoning with conditional ceteris paribus preference statements. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 71-80, Stockholm, 1999.

Digital Library

Google Scholar

[4]

Urszula Chajewska, Daphne Koller, and Ronald Parr. Making rational decision using adaptive utility elicitation. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 363-369, Austin, TX, 2000.

Digital Library

Google Scholar

[5]

Vašek Chvátal. Linear Programming. W. H. Freeman and Company, New York, 1983.

Google Scholar

[6]

Rina Dechter. Bucket elimination: A unifying framework for probabilistic inference. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, pages 211-219, Portland, OR, 1996.

Digital Library

Google Scholar

[7]

Rina Dechter. Mini-buckets: A general scheme for generating approximations in automated reasoning in probabilistic inference. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 1297-1302, Nagoya, 1997.

Digital Library

Google Scholar

[8]

C. Domshlak, R. I. Brafman, and E. S. Shimony. Preference-based configuration of web-page content. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, Seattle, 2001. To Appear.

Digital Library

Google Scholar

[9]

Chelsea C. White III, Andrew p. Sage, and Shigeru Dozono. A model of multiattribute decisionmaking and trade-off weight determination under uncertainty. IEEE Transactions on Systems, Man and Cybernetics, 14(2):223-229, 1984.

Google Scholar

[10]

R. L. Keeney mid H. Raiffa. Decisions with Multiple Objectives: Preferences and Value Trade-offs. Wiley, New York, 1976.

Google Scholar

[11]

Piero La Mura and Yoav Shoham. Expected utility networks. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 366- 373, Stockholm, 1999.

Digital Library

Google Scholar

[12]

Leonard J. Savage. The Foundations of Statistics. Wiley, New York, 1954.

Google Scholar

[13]

Nevin L. Zhang and David Poole. Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 5:301-328, 1996.

Digital Library

Google Scholar

Cited By

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Cornelio CGoldsmith JGrandi UMattei NRossi FVenable K(2022)Reasoning with PCP-NetsJournal of Artificial Intelligence Research10.1613/jair.1.1300972(1103-1161)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.13009
Laing KThwaites PGosling J(2019)Rank pruning for dominance queries in CP-netsJournal of Artificial Intelligence Research10.1613/jair.1.1132364:1(55-107)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11323
Oguego CAugusto JMuoz ASpringett M(2018)Using argumentation to manage users preferencesFuture Generation Computer Systems10.1016/j.future.2017.09.04081:C(235-243)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1016/j.future.2017.09.040
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UCP-networks: a directed graphical representation of conditional utilities

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Information

Published In

UAI'01: Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence

August 2001

588 pages

ISBN:1558608001

Editors:
Jack Breese
Microsoft Research, Redmond, Washington
,
Daphne Koller
Stanford University, Stanford, California

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 02 August 2001

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Cornelio CGoldsmith JGrandi UMattei NRossi FVenable K(2022)Reasoning with PCP-NetsJournal of Artificial Intelligence Research10.1613/jair.1.1300972(1103-1161)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.13009
Laing KThwaites PGosling J(2019)Rank pruning for dominance queries in CP-netsJournal of Artificial Intelligence Research10.1613/jair.1.1132364:1(55-107)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11323
Oguego CAugusto JMuoz ASpringett M(2018)Using argumentation to manage users preferencesFuture Generation Computer Systems10.1016/j.future.2017.09.04081:C(235-243)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1016/j.future.2017.09.040
Dragone PCremonesi PRicci FBerkovsky STuzhilin A(2017)Constructive RecommendationProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109867(441-445)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109867
Eichhorn CFey MKern-Isberner G(2016)CP- and OCF-networks - a comparisonFuzzy Sets and Systems10.1016/j.fss.2016.04.006298:C(109-127)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.fss.2016.04.006
Gölcük İBaykasoğlu A(2016)An analysis of DEMATEL approaches for criteria interaction handling within ANPExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.10.04146:C(346-366)Online publication date: 15-Mar-2016
https://dl.acm.org/doi/10.1016/j.eswa.2015.10.041
Pigozzi GTsoukiàs AViappiani P(2016)Preferences in artificial intelligenceAnnals of Mathematics and Artificial Intelligence10.1007/s10472-015-9475-577:3-4(361-401)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s10472-015-9475-5
Liu WWu CFeng BLiu J(2015)Conditional preference in recommender systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.08.04442:2(774-788)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.08.044
Benabbou N(2015)Possible Optimality and Preference Elicitation for Decision MakingProceedings of the 4th International Conference on Algorithmic Decision Theory - Volume 934610.1007/978-3-319-23114-3_34(561-566)Online publication date: 27-Sep-2015
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Ognjanović IGašEvić DBagheri E(2013)A stratified framework for handling conditional preferencesExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.08.02640:4(1094-1115)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.eswa.2012.08.026
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