Submodel Decomposition for Solving Limited Memory Influence Diagrams (Student Abstract)

Authors

  • Junkyu Lee University of California, Irvine

DOI:

https://doi.org/10.1609/aaai.v34i10.7198

Abstract

This paper presents a systematic way of decomposing a limited memory influence diagram (LIMID) to a tree of single-stage decision problems, or submodels and solving it by message passing. The relevance in LIMIDs is formalized by the notion of the partial evaluation of the maximum expected utility, and the graph separation criteria for identifying submodels follow. The submodel decomposition provides a graphical model approach for updating the beliefs and propagating the conditional expected utilities for solving LIMIDs with the worst-case complexity bounded by the maximum treewidth of the individual submodels.

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Published

2020-04-03

How to Cite

Lee, J. (2020). Submodel Decomposition for Solving Limited Memory Influence Diagrams (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13851-13852. https://doi.org/10.1609/aaai.v34i10.7198

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Section

Student Abstract Track