Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Assumptions, beliefs and probabilities
Artificial Intelligence
A logic-based analysis of Dempster-Shafer theory
International Journal of Approximate Reasoning
A generalization of the algorithm of Heidtmann to non-monotone formulas
Journal of Computational and Applied Mathematics
Model-based diagnostics and probabilistic assumption-based reasoning
Artificial Intelligence
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
Information Algebras: Generic Structures for Inference
Information Algebras: Generic Structures for Inference
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The general concept of probabilistic argumentation systems PAS is restricted to the two types of variables: assumptions, which model the uncertain part of the knowledge, and propositions, which model the rest of the information. Here, we introduce a third kind into PAS: so-called decision variables. This new kind allows to describe the decisions a user can make to react on some state of the system. Such a decision allows then possibly to reach a certain goal state of the system. Further, we present an algorithm, which exploits the special structure of PAS with decision variables. Some results related with this paper were published in (Anrig and Baziukaitė, 2003a).