Learning a decision maker's utility function from (possibly) inconsistent behavior

Artificial Intelligence

Volumn 160, Issue 1-2, 2004, Pages 53-78

  Nielsen, Thomas D ^a   Jensen, Finn V ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

Inconsistent behavior; Influence diagram; Learning utility functions

Indexed keywords

DATABASE SYSTEMS; DECISION MAKING; FUNCTIONS; LEARNING ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY;

BEHAVIORAL PATTERNS; INCONSISTENT BEHAVIOR; INFLUENCE DIAGRAM; LEARNING UTILITY FUNCTIONS;

LEARNING SYSTEMS;

EID: 7544219573     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2004.08.003     Document Type: Article

References (28)

1. M. Allais, The foundation of a positive theory of choice involving risk and a criticism of the postulate and axioms of the American school, in: Expected Utility Hypotheses and the Allais Paradox, Dordrecht, Holland, 1979, pp. 27-145. Original work published in 1952.
2. D. Applegate, R. Kannan, Sampling and integration of near log-concave functions, in: Proceedings of the 23rd Annual ACM Symposium on Theory and Computing, New Orleans, LA, 1991, pp. 156-163.
3. R.E. Bellman, Dynamic Programming, Princeton University Press, Princeton, NJ, 1957.
4. W.L. Buntine, A guide to the literature on learning probabilistic networks from data, IEEE Trans. Knowledge Data Engrg. 8 (1996) 195-210.
5. U. Chajewska, L. Getoor, J. Norman, Y. Shahar, Utility elicitation as a classification problem, in: G.F. Cooper, S. Moral (Eds.), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998, pp. 79-88.
6. U. Chajewska, D. Koller, Utilities as random variables: density estimation and structure discovery, in: C. Boutilier, M. Goldszmidt (Eds.), Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Stanford, CA, 2000, pp. 63-71.
7. U. Chajewska, D. Koller, D. Ormoneit, Learning an agent's utility function by observing behavior, in: Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, 2001, pp. 35-42.
8. U. Chajewska, D. Koller, R. Parr, Making rational decisions using adaptive utility elicitation, in: Proceedings of the Seventeenth National Conference on Artificial Intelligence, Austin, TX, 2000, pp. 363-369.
9. J.C. Felli, G.B. Hazen, Sensitivity analysis and the expected value of perfect information, Medical Decision Making 18 (1999) 95-109.
10. R.A. Howard, J.E. Matheson, Influence diagrams, in: R.A. Howard, J.E. Matheson (Eds.), The Principles and Applications of Decision Analysis, vol. 2, Strategic Decision Group, 1981, pp. 721-762, Chapter 37.
11. F. Jensen, F.V. Jensen, S.L. Dittmer, From influence diagrams to junction trees, in: R.L. de Mantaras, D. Poole (Eds.), Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, Morgan Kaufmann, San Mateo, CA, 1994, pp. 367-373.
12. F.V. Jensen, Bayesian Networks and Decision Graphs, Springer, New York, ISBN: 0-387-95259-4, 2001.
13. N.L. Johnson, S. Kotz, N. Balakrishnan, Continuous Univariate Distributions, second ed., Wiley Series in Probability and Statistics, vol. 1, Wiley, New York, 1994.
14. D. Kahneman, A. Tversky, Prospect theory, an analysis of decision under risk, Econometrica 47 (1979) 263-291
15. M.P. Keane, K.I. Wolpin, The career decisions of young men, J. Political Economy 105 (3) (1997) 473-522.
16. S.L. Lauritzen, D. Nilsson, Representing and solving decision problems with limited information, Management Sci. 47 (9) (2001) 1235-1251.
17. A.L. Madsen, F.V. Jensen, Lazy evaluation of symmetric Bayesian decision problems, in: K.B. Laskey, H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, Morgan Kaufmann, San Mateo, CA, 1999, pp. 382-390.
18. A.Y. Ng, S. Russell, Algorithms for inverse reinforcement learning, in: Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, 2000, pp. 663-670.
19. T.D. Nielsen, F.V. Jensen, Welldefined decision scenarios, in: K.B. Laskey, H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, Morgan Kaufmann, San Mateo, CA, 1999, pp. 502-511.
20. T.D. Nielsen, F.V. Jensen, Sensitivity analysis in influence diagrams, IEEE Trans. Systems Man Cybernet. Part A: Systems and Humans 33 (2) (2003) 223-234.
21. J. Quiggin, A theory of anticipated utility, J. Economic Behavior and Organisation 3 (4) (1982) 323-343.
22. J. Rust, Structural estimation of Markov decision processes, in: R.F. Engle, D.L. McFadden (Eds.), Handbook of Econometrics, vol. IV, Elsevier Science, Amsterdam, 1994, pp. 3081-3143.
23. T.J. Sargent, Estimation of dynamic labor demand schedules under rational expectations, J. Political Economy 86 (6) (1978) 1009-1044.
24. R.D. Shachter, Evaluating influence diagrams, Oper. Res. Soc. Amer. 34 (6) (1986) 79-90.
25. R.D. Shachter, Efficient value of information computation, in: K.B. Laskey, H. Prade (Eds.), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, Morgan Kaufmann, San Mateo, CA, 1999, pp. 594-601.
26. R.D. Shachter, M. Mandelbaum, A measure of decision flexibility, in: E. Horvitz, F.V. Jensen (Eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, Morgan Kaufmann, San Mateo, CA, 1996, pp. 485-491.
27. D. Suryadi, P.J. Gmytrasiewicz, Learning models of other agents using influence diagrams, in: Proceedings of the Seventh International Conference on User Modeling, 1999, pp. 223-232.
28. J.A. Tatman, R.D. Shachter, Dynamic programming and influence diagrams, IEEE Trans. Systems Man Cybernet. 20 (2) (1990) 365-379.