Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms
Fundamenta Informaticae - Special issue on soft computing
Stochastic dynamic programming with factored representations
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Possibilistic Influence Diagrams
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Decision with uncertainties, feasibilities, and utilities: towards a unified algebraic framework
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Decomposition of multi-operator queries on semiring-based graphical models
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Inferring rival's private information in game circumstance
ICNC'09 Proceedings of the 5th international conference on Natural computation
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
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In this article we present the framework of Possibilistic Influence Diagrams (PID), which allows 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 the two possibilistic qualitative utility criteria (optimistic and pessimistic) for evaluating strategies in a PID. We then describe decision tree-based methods for evaluating PID and computing optimal strategies and we study the computational complexity of PID optimisation problems for both cases. Finally, we propose a dedicated variable elimination algorithm that can be applied to both optimistic and pessimistic cases for solving PID.