Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A valuation-based language for expert systems
International Journal of Approximate Reasoning
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
On Spohn's rule for revision of beliefs
International Journal of Approximate Reasoning
Non-monotonic reasoning and the reversibility of belief change
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A symbolic generalization of probability theory
A symbolic generalization of probability theory
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
On the logic of iterated belief revision
TARK '94 Proceedings of the 5th conference on Theoretical aspects of reasoning about knowledge
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A symbolic generalization of probability theory
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Enhanced qualitative probabilistic networks for resolving trade-offs
Artificial Intelligence
Type uncertainty in ontologically-grounded qualitative probabilistic matching
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We study the connection between kappa calculus and probabilistic reasoning in diagnosis applications. Specifically, we abstract a probabilistic belief network for diagnosing faults into a kappa network and compare the ordering of faults computed using both methods. We show that, at least for the example examined, the ordering of faults coincide as long as all the causal relations in the original probabilistic network are taken into account. We also provide a formal analysis of some network structures where the two methods will differ. Both kappa rankings and infinitesimal probabilities have been used extensively to study default reasoning and belief revision. But little has been done on utilizing their connection as outlined above. This is partly because the relation between kappa and probability calculi assumes that probabilities are arbitrarily close to one (or zero). The experiments in this paper investigate this relation when this assumption is not satisfied. The reported results have important implications on the use of kappa rankings to enhance the knowledge engineering of uncertainty models.