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Title: A New Bounding Scheme for Influence Diagrams
Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. Computing the maximum expected utility (MEU) and the optimizing policy is exponential in the constrained induced width and therefore is notoriously difficult for larger models. In this paper, we develop a new bounding scheme for MEU that applies partitioning based approximations on top of the decomposition scheme called a multi-operator cluster DAG for influence diagrams that is more sensitive to the underlying structure of the model than the classical join-tree decomposition of influence diagrams. Our bounding scheme utilizes a cost-shifting mechanism to tighten the bound further. We demonstrate the effectiveness of the proposed scheme on various hard benchmarks.  more » « less
Award ID(s):
2008516
PAR ID:
10564839
Author(s) / Creator(s):
; ;
Publisher / Repository:
{AAAI} Press
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
35
Issue:
13
ISSN:
2159-5399
Page Range / eLocation ID:
12158 to 12165
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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