This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work pre-sented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantied with probability, possibility and Dempster-Shafer belief values. The use of these forms of reasoning, which include the phenomenon of \explaining away", is illustrated with the use of a medical example
This section investigates graphical modeling as a powerful framework for drawing inferences under im...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
This paper presents some results concerning the qualitative behaviour of possibilistic networks. The...
Qualitative probabilistic networks represent prob-abilistic influences between variables. Due to the...
This research addresses two intensive computational problems of reasoning under uncertainty in artif...
Qualitative methods for reasoning under uncer-tainty may be helpful in situations where quan-tificat...
Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or ...
AbstractQualitative probabilistic networks are qualitative abstractions of probabilistic networks, s...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
AbstractQualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian ...
AbstractQualitative probabilistic networks were designed to overcome, to at least some extent, the q...
AbstractIn recent years, several papers have described systems for plausible reasoning which do not ...
Special session organizers: Salem Benferhat and Henri PradeInternational audienceMany approaches hav...
This section investigates graphical modeling as a powerful framework for drawing inferences under im...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
This paper extends previous work on propagating qualitative uncertainty in networks in which a gener...
This paper presents some results concerning the qualitative behaviour of possibilistic networks. The...
Qualitative probabilistic networks represent prob-abilistic influences between variables. Due to the...
This research addresses two intensive computational problems of reasoning under uncertainty in artif...
Qualitative methods for reasoning under uncer-tainty may be helpful in situations where quan-tificat...
Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or ...
AbstractQualitative probabilistic networks are qualitative abstractions of probabilistic networks, s...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
AbstractQualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian ...
AbstractQualitative probabilistic networks were designed to overcome, to at least some extent, the q...
AbstractIn recent years, several papers have described systems for plausible reasoning which do not ...
Special session organizers: Salem Benferhat and Henri PradeInternational audienceMany approaches hav...
This section investigates graphical modeling as a powerful framework for drawing inferences under im...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...