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Optimizing the Hurwicz Criterion in Decision Trees with Imprecise Probabilities

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Algorithmic Decision Theory (ADT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

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Abstract

This paper is devoted to sequential decision problems with imprecise probabilities. We study the problem of determining an optimal strategy according to the Hurwicz criterion in decision trees. More precisely, we investigate this problem from the computational viewpoint. When the decision tree is separable (to be defined in the paper), we provide an operational approach to compute an optimal strategy, based on a bicriteria dynamic programming procedure. The results of numerical tests are presented. When the decision tree is non-separable, we prove the NP-hardness of the problem.

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References

  1. Bielza, C., Rios Insua, D., Rios-Insua, S.: Influence diagrams under partial information. In: Bayesian Statistics, Oxford, U.P, vol. 5, pp. 491–497 (1996)

    Google Scholar 

  2. de Campos, C.P., Kikuti, D., Cozman, F.G.: Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities. In: IJCAI Workshop on Advances in Preference Handling (2005)

    Google Scholar 

  3. Howard, R., Matheson, J.: Influence Diagrams. Strategic Decisions Group, Menlo Park (1984)

    Google Scholar 

  4. Jaffray, J.-Y., Jeleva, M.: Information processing under imprecise risk with the hurwicz criterion. In: 5th International Symposium on Imprecise Probability: Theories and Applications, pp. 233–242 (2007)

    Google Scholar 

  5. Kasperski, A.: Discrete Optimization with Interval Data: Minmax Regret and Fuzzy Approach. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  6. LaValle, I.H., Wapman, K.R.: Rolling back decision trees requires the independence axiom. Management Science 32(3), 382–385 (1986)

    Article  MathSciNet  Google Scholar 

  7. McClennen, E.F.: Rationality and Dynamic choice: Foundational Explorations. Cambridge University Press, Cambridge (1990)

    Book  Google Scholar 

  8. Puterman, M.L.: Markov Decision Processes - Discrete Stochastic Dynamic Programming. Wiley & Sons, Chichester (1994)

    MATH  Google Scholar 

  9. Raiffa, H.: Decision Analysis: Introductory Lectures on Choices under Uncertainty. Addison-Wesley, Reading (1968)

    MATH  Google Scholar 

  10. Shachter, R.: Evaluating influence diagrams. Operations Research 34, 871–882 (1986)

    Article  MathSciNet  Google Scholar 

  11. von Neuman, J., Morgenstern, O.: Theory of games and economic behaviour. Princeton University Press, Princeton (1947)

    Google Scholar 

  12. Walley, P.: Statistical reasoning with imprecise probabilities. Monographs on statistics and applied probability, vol. 91. Chapman and Hall, Boca Raton (1991)

    Book  Google Scholar 

  13. Weichselberger, K.: The theory of interval-probability as a unifying concept for uncertainty. In: 1st International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA), pp. 387–396 (1999)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Jeantet, G., Spanjaard, O. (2009). Optimizing the Hurwicz Criterion in Decision Trees with Imprecise Probabilities. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_30

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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