Abstract
This paper considers the task of learning utility functions of certain influence diagram based on the decision maker’s past decisions. We assume that the influence diagram structure and the probability distribution it assigns to random events are known, so that we need only infer the utility function u for its. We also assume that the decision maker is rational. In particular, the decision maker’s past decisions can be viewed as constraints on u. So, if we have a prior probability distribution p(u) over u, we can then condition on these constraints to obtain u. In this paper, an approach for learning utility functions from decision maker’s behavior was proposed. We also show that it is effective.
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References
Howard, R.A., Matheson, J.E.: Influence diagram. In: Howard, R.A., Matheson, J.E. (eds.) The Principles and Applications of Decision Analysis, vol. 2, pp. 721–762. Strategic Decision Group (1981)
Kim, J.K., Lee, K.C., Lee, J.K.: Hybrid of neural network and decision knowledge approach to generating influence diagrams. Expert Syst. Appl. 23, 237–244 (2002)
Kim, J.K., Chu, S.C.: Sensitivity analysis in the decision class analysis using neural networks. In: 4th World Congress on Expert Systems, Mexico, pp. 874–879 (1998)
Bai, L., Liu, W.Y.: An influence diagram structure learning algorithm based on scoring-search. In: 10th Joint International Computer Conference, pp. 100–104. World Publishing Corporation, Kunming (2004)
Ng, A.Y., Russell, S.: Algorithms for inverse reinforcement leaning. In: 17th International Conference on Machine Learning, Stanford, pp. 663–670 (2000)
Chajewska, U., Koller, D., Ormoneit, D.: Learning an agent’s utility function by observing behavior. In: 18th International Conference on Machine Learning, Williamstown, MA, pp. 35–42 (2001)
Nielsen, T.D., Jensen, F.V.: Learning a decision maker’s utility function from (possibly) inconsistent behavior. Artif. Intell. 160(1), 53–78 (2004)
Tatman, J.A., Shachter, R.D.: Dynamic programming and influence diagrams. IEEE Trans. Syst. Man Cybern. 20, 265–279 (1990)
Pearl, J.: Probabilistic Reasoning in Intelligence Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Los Altos (1988)
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Lei, B. (2020). Learning Influence Diagram Utility Function by Observing Behavior. In: Park, J., Yang, L., Jeong, YS., Hao, F. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2019 2019. Lecture Notes in Electrical Engineering, vol 590. Springer, Singapore. https://doi.org/10.1007/978-981-32-9244-4_23
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DOI: https://doi.org/10.1007/978-981-32-9244-4_23
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