Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth
SIAM Journal on Computing
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Information Algebras: Generic Structures for Inference
Information Algebras: Generic Structures for Inference
Representing and Solving Decision Problems with Limited Information
Management Science
Influence Diagrams for Team Decision Analysis
Decision Analysis
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
Multiobjective optimization using GAI models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
New complexity results for MAP in Bayesian networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Approximation algorithms for max-sum-product problems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 1064 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.