Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Stochastic dynamic programming with factored representations
Artificial Intelligence
Nonapproximability results for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Complexity results and algorithms for possibilistic influence diagrams
Artificial Intelligence
Graph transformation based reduction analysis of PID
ACM SIGSOFT Software Engineering Notes
Necessity-based Choquet integrals for sequential decision making under uncertainty
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
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In this article we present the framework of Possibilistic Influence Diagrams (PID), which allow to model in a compact form problems of sequential decision making under uncertainty, when only ordinal data on transitions likelihood or preferences are available. The graphical part of a PID is exactly the same as that of usual influence diagrams, however the semantics differ. Transition likelihoods are expressed as possibility distributions and rewards are here considered as satisfaction degrees. Expected utility is then replaced by anyone of two possibilistic qualitative utility criteria for evaluating strategies in a PID. We describe a decision tree-based method for evaluating PID and computing optimal strategies. We then study the computational complexity of PID-related problems (computation of the value of a policy, computation of an optimal policy).