Unstructuring user preferences: Efficient non-parametric utility revelation

Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005

Volumn , Issue , 2005, Pages 169-177

  Domshlak, Carmel ^a   Joachims, Thorsten ^b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b School of Operations Research and Information Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NON-PARAMETRIC; NOVEL METHODOLOGY; ORDINAL PREFERENCE; PREFERENCE STATEMENTS; UTILITY FUNCTIONS;

ARTIFICIAL INTELLIGENCE;

EID: 78751697759     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. F. Bacchus and A. Grove. Graphical models for preference and utility. In UAI-95, pages 3-10, 1995.
2. D. Bertsekas, A. Nedic, and A. Ozdaglar. Convex Analysis and Optimization. Athena Scientific, 2003.
3. C. Boutilier, F. Bacchus, and R. I. Brafman. UCP-networks: A directed graphical representation of conditional utilities. In UAI-01, pages 56-64, 2001.
4. C. Boutilier, R. Brafman, C. Domshlak, H. Hoos, and D. Poole. CP-nets: A tool for representing and reasoning about conditional ceteris paribus preference statements. J. Artif. Intel. Res., 21:135-191, 2004. (Pubitemid 41525900)
5. R. Brafman, C. Domshlak, and T. Kogan. Compact value-function representations for qualitative preferences. In UAI-04, 2004.
6. R. M. Chisholm and E. Sosa. On the logic of 'Intrinsically Better'. American Philosophical Quarterly, 3:244-249, 1966.
7. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning Journal, 20:273-297, 1995.
8. J. Doyle. Prospects for preferences. Computational Intelligence, 20(2):111-136, 2004.
9. J. Doyle and R. H. Thomason. Background to qualitative decision theory. AI Magazine, 20(2):55-68, 1999.
10. P. E. Green, A. M. Krieger, and Y. Wind. Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3):56-73, 2001.
11. V. Ha and P. Haddawy. A hybrid approach to reasoning with partially elicited preference models. In UAI-99, pages 263-270, 1999.
12. S. O. Hansson. The Structure of Values and Norms. Cambridge University Press, 2001.
13. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, pages 115-132. 2000.
14. R. L. Keeney and H. Raiffa. Decision with Multiple Objectives. Wiley, 1976.
15. D. H. Krantz, R. D. Luce, P. Suppes, and A. Tversky. Foundations of Measurement. Academic, 1971.
16. P. La Mura. Foundations of Multi-Agent Systems. PhD thesis, Graduate School of Business, Stanford, 1999.
17. P. La Mura and Y. Shoham. Expected utility networks. In UAI-99, pages 367-373, 1999.
18. M. McGeachie and J. Doyle. Utility functions for ceteris paribus preferences. Computational Intelligence, 20(2):158-217, 2004.
19. D. J. Packard. A preference logic minimally complete for expected utility maximization. Journal of Philosophical Logic, 4(2):223-235, 1975.
20. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
21. Y. Shoham. A symmetric view of probabilities and utilities. In IJCAI-97, pages 1324-1329, 1997.
22. V. Vapnik. Statistical Learning Theory. Wiley, 1998.