Belief structures, possibility theory and decomposable confidence measures on finite sets
Computers and Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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First-order conditional logic for default reasoning revisited
ACM Transactions on Computational Logic (TOCL)
Plausibility measures and default reasoning
Journal of the ACM (JACM)
The semantics of preference-based belief operators
Proceedings of the 9th conference on Theoretical aspects of rationality and knowledge
Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
Great expectations: part II: Generalized expected utility as a universal decision rule
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Linguistic quantifiers modeled by Sugeno integrals
Artificial Intelligence
Decision with uncertainties, feasibilities, and utilities: towards a unified algebraic framework
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An Axiomatic Approach to Qualitative Decision Theory with Binary Possibilistic Utility
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A General Model for Epistemic State Revision using Plausibility Measures
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Conditional plausibility measures and Bayesian networks
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Journal of Artificial Intelligence Research
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Journal of Artificial Intelligence Research
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Journal of Artificial Intelligence Research
Great expectations: part I: on the customizability of generalized expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Great expectations: part II: generalized expected utility as a universal decision rule
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Plausibility measures: a general approach for representing uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Algebraic Markov decision processes
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Linguistic quantifiers modeled by Sugeno integrals
Artificial Intelligence
Perceiving environments for intelligent agents
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Plausibility measures and default reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
First-order conditional logic revisited
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Conditional plausibility measures and Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A qualitative Markov assumption and its implications for belief change
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an "as needed" basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their "algebraic properties", analogues to the use of + and × in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool.