Semantical considerations on nonmonotonic logic
Artificial Intelligence
Artificial Intelligence
A logic to reason about likelihood
Artificial Intelligence
Nonmonotonic logic and temporal projection
Artificial Intelligence
An approach to default reasoning based on a first-order conditional logic: revised report
Artificial Intelligence
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
What the lottery paradox tells us about default reasoning
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Thoughts and afterthoughts on the 1988 Workshop on Principles of Hybrid Reasoning
AI Magazine - Reports from three of the 1990 Spring symposia and eight workshops held over the past two years
Probabilsitic semantics and defaults
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Deciding Consistency of Databases Containing Defeasible and Strict Information
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Classifiers: a theoretical and empirical study
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Nonmonotonicity and the scope of reasoning: preliminary report
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A maximum entropy approach to nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
The future of knowledge representation
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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This paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature of plausible reasoning. These issues have a common underlying theme: there should be an underlying belief calculus of which the many different reasoning forms are special cases, sometimes approximate. The system presented allows reasoning about defaults, likelihood, necessity and possibility in a manner similar to the earlier work of Adams. The system is based on the belief calculus of subjective Bayesian probability which itself is based on a few simple assumptions about how belief should be manipulated. Approximations, semantics, consistency and consequence results are presented for the system. While this puts these often discussed plausible reasoning forms on a probabilistic footing, useful application to practical problems remains an issue.