Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Theoretical foundations for non-monotonic reasoning in expert systems
Logics and models of concurrent systems
Using crude probability estimates to guide diagnosis
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Inconsistency in possibilistic knowledge bases: to live with it or not live with it
Fuzzy logic for the management of uncertainty
Artificial Intelligence
Default reasoning and the transferable belief model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Inconsistency management and prioritized syntax-based entailment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Propositional non-monotonic reasoning and inconsistency in symmetric neural networks
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
System-Z+: a formalism for reasoning with variable-strength defaults
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Explaining default intuitions using maximum entropy
Journal of Applied Logic - Special issue on combining probability and logic
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We present a new approach to dealing with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' ε-semantics, are epsilon-belief assignments, where values committed to focal elements are either close to 0 or close to 1. We define two systems based on these structures, and relate them to other non-monotonic systems presented in the literature. We show that our second system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy.