Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
A forward-backward Monte Carlo method for solving influence diagrams
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Journal of Engineering and Technology Management
Simulation method for solving hybrid influence diagrams in decision making
Proceedings of the Winter Simulation Conference
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The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.