A New Bounding Scheme for Influence Diagrams
Keywords:Graphical Models, Sequential Decision Making
AbstractInfluence 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.
How to Cite
Marinescu, R., Lee, J., & Dechter, R. (2021). A New Bounding Scheme for Influence Diagrams. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12158-12165. https://doi.org/10.1609/aaai.v35i13.17443
AAAI Technical Track on Reasoning under Uncertainty