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Updates and Uncertainty in CP-Nets

  • Cristina Cornelio
  • Judy Goldsmith
  • Nicholas Mattei
  • Francesca Rossi
  • K. Brent Venable
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

Abstract

In this paper we present a two-fold generalization of conditional preference networks (CP-nets) that incorporates uncertainty. CP-nets are a formal tool to model qualitative conditional statements (cp-statements) about preferences over a set of objects. They are inherently static structures, both in their ability to capture dependencies between objects and in their expression of preferences over features of a particular object. Moreover, CP-nets do not provide the ability to express uncertainty over the preference statements. We present and study a generalization of CP-nets which supports changes and allows for encoding uncertainty, expressed in probabilistic terms, over the structure of the dependency links and over the individual preference relations.

Keywords

Preferences Graphical Models Probabilistic Reasoning CP-nets 

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References

  1. 1.
    Bigot, D., Fargier, H., Mengin, J., Zanuttini, B.: Probabilistic conditional preference networks. In: Proc. 29th Conf. on Uncertainty in Artificial Intelligence, UAI (2013)Google Scholar
  2. 2.
    Boutilier, C., Brafman, R., Domshlak, C., Hoos, H., Poole, D.: CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research 21, 135–191 (2004)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Proc. 18th AAAI Conference on Artificial Intelligence, pp. 239–246 (2002)Google Scholar
  4. 4.
    D’Ambrosio, B.: Inference in Bayesian Networks. AI Magazine 20(2), 21 (1999)Google Scholar
  5. 5.
    Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artificial Intelligence 113(1-2), 41–85 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Domshlak, C., Brafman, R.: CP-nets: Reasoning and consistency testing. In: Proc. 8th Intl. Conf. on Principles and Knowledge Representation and Reasoning, KRR (2002)Google Scholar
  7. 7.
    Faltings, B., Torrens, M., Pu, P.: Solution generation with qualitative models of preferences. Computational Intelligence 20(2), 246–263 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Fürnkranz, J., Hüllermeier, E.: Preference Learning: An Introduction. Springer (2010)Google Scholar
  9. 9.
    Goldsmith, J., Junker, U.: Preference handling for artificial intelligence. AI Magazine 29(4) (2009)Google Scholar
  10. 10.
    Goldsmith, J., Lang, J., Truszczynski, M., Wilson, N.: The computational complexity of dominance and consistency in CP-nets. Journal of Artificial Intelligence Research 33(1), 403–432 (2008)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Maran, A., Maudet, N., Pini, M.S., Rossi, F., Venable, K.B.: A framework for aggregating influenced CP-nets and its resistance to bribery. In: Proc. 27th AAAI Conference on Artificial Intelligence (2013)Google Scholar
  12. 12.
    Marden, J.I.: Analyzing and Modeling Rank Data. CRC Press (1995)Google Scholar
  13. 13.
    Mattei, N., Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting over combinatorial domains is easy. In: Proc. 11th Intl. Joint Conf. on Autonomous Agents and Multiagent Systems, AAMAS (2012)Google Scholar
  14. 14.
    Mattei, N., Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting with CP-nets. Annals of Mathematics and Artificial Intelligence (2013)Google Scholar
  15. 15.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)Google Scholar
  16. 16.
    Price, R., Messinger, P.R.: Optimal recommendation sets: Covering uncertainty over user preferences. In: Proc. 20th AAAI Conference on Artificial Intelligence, pp. 541–548 (2005)Google Scholar
  17. 17.
    Regenwetter, M., Dana, J., Davis-Stober, C.P.: Transitivity of preferences. Psychological Review 118(1) (2011)Google Scholar
  18. 18.
    Rossi, F., Venable, K., Walsh, T.: mCP nets: representing and reasoning with preferences of multiple agents. In: Proc. 19th AAAI Conference on Artificial Intelligence, pp. 729–734 (2004)Google Scholar
  19. 19.
    Roth, A.E., Kagel, J.H.: The handbook of experimental economics, vol. 1. Princeton University Press, Princeton (1995)Google Scholar
  20. 20.
    Tversky, A., Kahneman, D.: Judgement under uncertainty: Heuristics and biases. Science 185, 1124–1131 (1974)CrossRefGoogle Scholar
  21. 21.
    Xia, L., Conitzer, V., Lang, J.: Voting on multiattribute domains with cyclic preferential dependencies. In: Proc. 23rd AAAI Conference on Artificial Intelligence, pp. 202–207 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Cristina Cornelio
    • 1
  • Judy Goldsmith
    • 2
  • Nicholas Mattei
    • 3
  • Francesca Rossi
    • 1
  • K. Brent Venable
    • 4
  1. 1.University of PadovaItaly
  2. 2.University of KentuckyUSA
  3. 3.NICTA and UNSWAustralia
  4. 4.Tulane University and IHMCUSA

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