Automated Preference Elicitation for Decision Making

Part of the Studies in Computational Intelligence book series (SCI, volume 474)

Abstract

In the contemporary complex world decisions are made by an imperfect participant devoting limited deliberation resources to any decision-making task. A normative decision-making (DM) theory should provide support systems allowing such a participant to make rational decisions in spite of the limited resources. Efficiency of the support systems depends on the interfaces enabling a participant to benefit from the support while exploiting the gradually accumulating knowledge about DM environment and respecting incomplete, possibly changing, participant’s DM preferences. The insufficiently elaborated preference elicitation makes even the best DM supports of a limited use. This chapter proposes a methodology of automatic eliciting of a quantitative DM preference description, discusses the options made and sketches open research problems. The proposed elicitation serves to fully probabilistic design, which includes a standard Bayesian decision making.

Keywords

Bayesian decision making fully probabilistic design DM preference elicitation support of imperfect participants 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abramowitz, M., Stegun, I.: Handbook of Mathematical Functions. Dover Publications, New York (1972)Google Scholar
  2. 2.
    Barndorff-Nielsen, O.: Information and exponential families in statistical theory, New York (1978)Google Scholar
  3. 3.
    Berec, L., Kárný, M.: Identification of reality in Bayesian context. In: Warwick, K., Kárný, M. (eds.) Computer-Intensive Methods in Control and Signal Processing: Curse of Dimensionality, Birkhäuser, Boston, pp. 181–193 (1997)Google Scholar
  4. 4.
    Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985)MATHGoogle Scholar
  5. 5.
    Bernardo, J.M.: Expected information as expected utility. The Annals of Statistics 7(3), 686–690 (1979)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Bohlin, T.: Interactive System Identification: Prospects and Pitfalls. Springer, New York (1991)MATHCrossRefGoogle Scholar
  7. 7.
    Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Biundo, S. (ed.) AAAI-2002 Proc. of the Fifth European Conference on Planning, pp. 239–246. AAAI Press, Durham (2002)Google Scholar
  8. 8.
    Boutilier, C., Brafman, R., Geib, C., Poole, D.: A constraint-based approach to preference elicitation and decision making. In: Proceedings of AAAI Spring Symposium on Qualitative Decision Theory, Stanford, CA, pp. 19–28 (1997)Google Scholar
  9. 9.
    Bowong, S., Dimi, J., Kamgang, J., Mbang, J., Tewa, J.: Survey of recent results of multi-compartments intra-host models of malaria and HIV. Revue ARIMA 9, 85–107 (2008)MathSciNetGoogle Scholar
  10. 10.
    Chajewska, U., Koller, D.: Utilities as random variables: Density estimation and structure discovery. In: Proceedings of UAI 2000, pp. 63–71 (2000)Google Scholar
  11. 11.
    Cooke, N.: Varieties of knowledge elicitation techniques. International Journal of Human-Computer Studies 41, 801–849 (1994)MATHCrossRefGoogle Scholar
  12. 12.
    Feldbaum, A.: Theory of dual control. Autom. Remote Control 21(9) (1960)Google Scholar
  13. 13.
    Gajos, K., Weld, D.: Preference elicitation for interface optimization. In: Proceedings of UIST 2005 (2005)Google Scholar
  14. 14.
    Garthwaite, P., Kadane, J., O´Hagan, A.: Statistical methods for eliciting probability distributions. J. of the American Statistical Association 100(470), 680–700 (2005)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Guy, T.V., Kárný, M., Wolpert, D.H. (eds.): Decision Making with Imperfect Decision Makers. ISRL, vol. 28. Springer, Heidelberg (2012)MATHGoogle Scholar
  16. 16.
    Viappiani, H.P., Boutilier, S.Z., Learning, C.: complex concepts using crowdsourcing: A Bayesian approach. In: Proceedings of the Second Conference on Algorithmic Decision Theory (ADT 2011), Piscataway, NJ (2011)Google Scholar
  17. 17.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)MATHGoogle Scholar
  18. 18.
    Horst, R., Tuy, H.: Global Optimization, p. 727. Springer (1996)Google Scholar
  19. 19.
    Jacobs, O., Patchell, J.: Caution and probing in stochastic control. Int. J. of Control 16, 189–199 (1972)MATHCrossRefGoogle Scholar
  20. 20.
    Jaynes, E.: Information theory and statistical mechanics. Physical Review Series II 106(4), 620–630 (1957)MathSciNetMATHGoogle Scholar
  21. 21.
    Jimison, H., Fagan, L., Shachter, R., Shortliffe, E.: Patient-specific explanation in models of chronic disease. AI in Medicine 4, 191–205 (1992)Google Scholar
  22. 22.
    Jorgensen, S.B., Hangos, K.M.: Qualitative models as unifying modelling tool for grey box modelling. Int. J. of Adaptive Control and Signal Processing 9(6), 461–562 (1995)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kárný, M.: Towards fully probabilistic control design. Automatica 32(12), 1719–1722 (1996)MathSciNetMATHCrossRefGoogle Scholar
  24. 24.
    Kárný, M., Andrýsek, J., Bodini, A., Guy, T., Kracík, J., Nedoma, P., Ruggeri, F.: Fully probabilistic knowledge expression and incorporation. Tech. Rep. 8-10MI, CNR IMATI, Milan (2008)Google Scholar
  25. 25.
    Kárný, M., Andrýsek, J., Bodini, A., Guy, T.V., Kracík, J., Ruggeri, F.: How to exploit external model of data for parameter estimation? Int. J. of Adaptive Control and Signal Processing 20(1), 41–50 (2006)MATHCrossRefGoogle Scholar
  26. 26.
    Kárný, M., Böhm, J., Guy, T.V., Jirsa, L., Nagy, I., Nedoma, P., Tesař, L.: Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London (2006)Google Scholar
  27. 27.
    Kárný, M., Guy, T.: Preference elicitation in fully probabilistic design of decision strategies. In: Proc. of the 49th IEEE Conference on Decision and Control. IEEE (2010)Google Scholar
  28. 28.
    Kárný, M., Guy, T.V.: On Support of Imperfect Bayesian Participants. In: Guy, T.V., Kárný, M., Wolpert, D.H. (eds.) Decision Making with Imperfect Decision Makers. ISRL, vol. 28, pp. 29–56. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  29. 29.
    Kárný, M., Guy, T.V.: Fully probabilistic control design. Systems & Control Letters 55(4), 259–265 (2006)MathSciNetMATHCrossRefGoogle Scholar
  30. 30.
    Kárný, M., Halousková, A., Böhm, J., Kulhavý, R., Nedoma, P.: Design of linear quadratic adaptive control: Theory and algorithms for practice. Kybernetika 21(Supplement to Nos. 3, 4 ,5, 6) (1985)Google Scholar
  31. 31.
    Kárný, M., Kroupa, T.: Axiomatisation of fully probabilistic design. Information Sciences 186(1), 105–113 (2012)MathSciNetMATHCrossRefGoogle Scholar
  32. 32.
    Kárný, M., Kulhavý, R.: Structure determination of regression-type models for adaptive prediction and control. In: Spall, J. (ed.) Bayesian Analysis of Time Series and Dynamic Models. ch.12, Marcel Dekker, New York (1988)Google Scholar
  33. 33.
    Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. JohnWiley and Sons Inc. (1976)Google Scholar
  34. 34.
    Koopman, R.: On distributions admitting a sufficient statistic. Tran. of American Mathematical Society 39, 399 (1936)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Kuhn, H., Tucker, A.: Nonlinear programming. In: Proc. of the 2nd Berkeley Symposium, pp. 481–492. University of California Press, Berkeley (1951)Google Scholar
  36. 36.
    Kulhavý, R.: Can we preserve the structure of recursive Bayesian estimation in a limited-dimensional implementation? In: Helmke, U., Mennicken, R., Saurer, J. (eds.) Systems and Networks: Mathematical Theory and Applications, vol. I, pp. 251–272. Akademie Verlag, Berlin (1994)Google Scholar
  37. 37.
    Kulhavý, R., Zarrop, M.B.: On a general concept of forgetting. Int. J. of Control 58(4), 905–924 (1993)MATHCrossRefGoogle Scholar
  38. 38.
    Kullback, S., Leibler, R.: On information and sufficiency. Annals of Mathematical Statistics 22, 79–87 (1951)MathSciNetMATHCrossRefGoogle Scholar
  39. 39.
    Kumar, P.: A survey on some results in stochastic adaptive control. SIAM J. Control and Applications 23, 399–409 (1985)Google Scholar
  40. 40.
    Lainiotis, D.: Partitioned estimation algorithms, i: Nonlinear estimation. Information Sciences 7, 203–235 (1974)MathSciNetMATHCrossRefGoogle Scholar
  41. 41.
    Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The automated travel assistant. In: Proceedings of User Modelling 1997 (1997)Google Scholar
  42. 42.
    Loeve, M.: Probability Theory. van Nostrand, Princeton (1962), Russian translation, MoscowGoogle Scholar
  43. 43.
    Nagy, I., Suzdaleva, E., Kárný, M., Mlynářová, T.: Bayesian estimation of dynamic finite mixtures. Int. Journal of Adaptive Control and Signal Processing 25(9), 765–787 (2011)MATHCrossRefGoogle Scholar
  44. 44.
    Nelsen, R.: An Introduction to Copulas. Springer, New York (1999)MATHCrossRefGoogle Scholar
  45. 45.
    O’Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J., Rakow, T.: Uncertain judgement: eliciting experts’ probabilities. John Wiley & Sons (2006)Google Scholar
  46. 46.
    Osborne, M., Rubinstein, A.: A course in game theory. MIT Press (1994)Google Scholar
  47. 47.
    Peterka, V.: Bayesian system identification. In: Eykhoff, P. (ed.) Trends and Progress in System Identification, pp. 239–304. Pergamon Press, Oxford (1981)Google Scholar
  48. 48.
    Rao, M.: Measure Theory and Integration. John Wiley, New York (1987)MATHGoogle Scholar
  49. 49.
    Savage, L.: Foundations of Statistics. Wiley, New York (1954)MATHGoogle Scholar
  50. 50.
    Shi, R., MacGregor, J.: Modelling of dynamic systems using latent variable and subspace methods. J. of Chemometrics 14(5-6), 423–439 (2000)CrossRefGoogle Scholar
  51. 51.
    Shore, J., Johnson, R.: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Tran. on Information Theory 26(1), 26–37 (1980)MathSciNetMATHCrossRefGoogle Scholar
  52. 52.
    Si, J., Barto, A., Powell, W., Wunsch, D. (eds.): Handbook of Learning and Approximate Dynamic Programming. Wiley-IEEE Press, Danvers (2004)Google Scholar
  53. 53.
    Šindelář, J., Vajda, I., Kárný, M.: Stochastic control optimal in the Kullback sense. Kybernetika 44(1), 53–60 (2008)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

Personalised recommendations