Abstract
Influence Diagrams are a tool used to represent and solve decision problems under uncertainty. One of the most efficient exact methods used to evaluate Influence Diagrams is Lazy Evaluation. This paper proposes the use of trees for representing potentials involved in an Influence Diagram in order to obtain an approximate Lazy Evaluation of decision problems. This method will allow to evaluate complex decision problems that are not evaluable with exact methods due to their computational cost. The experimental work compares the efficiency and goodness of the approximate solutions obtained using different kind of trees.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Howard, R.A., Matheson, J.E.: Influence diagram retrospective. Decision Analysis 2(3), 144–147 (2005)
Jensen, F., Jensen, F.V., Dittmer, S.L.: From Influence Diagrams to junction trees. In: Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence, pp. 367–373. Morgan Kaufmann Publishers Inc. (1994)
Jensen, F., Nielsen, T.: Bayesian networks and decision graphs. Springer (2007)
Shachter, R.: Evaluating influence diagrams. Operations Research, 871–882 (1986)
Madsen, A., Jensen, F.: Lazy evaluation of symmetric Bayesian decision problems. In: Proceedings of the 15th Conference on Uncertainty in AI, pp. 382–390. Morgan Kaufmann Publishers Inc. (1999)
Gómez, M., Cano, A.: Applying numerical trees to evaluate asymmetric decision problems. In: ESCQARU, pp. 196–207 (2003)
Cabañas, R., Gómez, M., Cano, A.: Approximate inference in influence diagrams using binary trees. In: Proceedings of the 6th European Workshop on Probabilistic Graphical Models, PGM 2012, pp. 43–50 (2011)
Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Proceedings of the 12th International Conference on Uncertainty in AI, pp. 115–123. Morgan Kaufmann Publishers Inc. (1996)
Cano, A., Moral, S., Salmerón, A.: Penniless propagation in join trees. International Journal of Intelligent Systems 15(11), 1027–1059 (2000)
Madsen, A., Jensen, F.: Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113(1-2), 203–245 (1999)
Wong, S., Butz, C.: Contextual weak independence in Bayesian networks. In: Proceedings of the 15th Conference on Uncertainty in AI, pp. 670–679. Morgan Kaufmann Publishers Inc. (1999)
Lucas, P., Taal, B.: Computer-based decision support in the management of primary gastric non-hodgkin lymphoma. UU-CS (1998-33) (1998)
Zitzler, E., Brockhoff, D., Thiele, L.: The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)
Bielza, C., Shenoy, P.: A comparison of graphical techniques for asymmetric decision problems. Management Science 45(11), 1552–1569 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cabañas, R., Cano, A., Gómez-Olmedo, M., Madsen, A.L. (2013). Approximate Lazy Evaluation of Influence Diagrams. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40643-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40642-3
Online ISBN: 978-3-642-40643-0
eBook Packages: Computer ScienceComputer Science (R0)