Complexity results and algorithms for possibilistic influence diagrams
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...
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| Veröffentlicht in: | Artificial intelligence 2008-05, Vol.172 (8), p.1018-1044 |
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| container_title | Artificial intelligence |
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| creator | Garcia, Laurent Sabbadin, Régis |
| description | 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. |
| doi_str_mv | 10.1016/j.artint.2007.11.008 |
| format | Article |
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| source | Elsevier ScienceDirect Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Causal networks Computer Science Computer science control theory systems Decision theory Decision theory. Utility theory Exact sciences and technology Influence diagrams Learning and adaptive systems Mathematics Operational research and scientific management Operational research. Management science Possibility theory Theoretical computing |
| title | Complexity results and algorithms for possibilistic influence diagrams |
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