Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality and maximum entropy updating
International Journal of Approximate Reasoning
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Defaults and probabilities: extensions and coherence
Proceedings of the first international conference on Principles of knowledge representation and reasoning
What does a conditional knowledge base entail?
Artificial Intelligence
Asymptomatic conditional probabilities for first-order logic
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
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
Plausibility measures and default reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Defaults and infinitesimals defeasible inference by nonarchimedean entropy maximization
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Sequential thresholds: context sensitive default extensions
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Entailment in probability of thresholded generalizations
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Generating new beliefs from old
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
A logic for default reasoning about probabilities
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
The logic of risky knowledge, reprised
International Journal of Approximate Reasoning
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We describe a new approach to default, reasoning, based on a principle on indifference among possible worlds. We interpret default rules as extreme statistical statements, thus obtaining a knowledge base KB comprised of statistical and first-order statements. We then assign equal probability to all worlds consistent with KB in order to assign a degree of belief to a statement Φ. The degree of belief can be used to decide whether to defeasibly conclude Φ. Various natural patterns of reasoning, such as a preference for more specific defaults, indifference to irrelevant information, and the ability to combine independent pieces of evidence, turn out to follow naturally from this technique. Furthermore, our approach is not restricted to default reasoning; it supports a spectrum of reasoning, from quantitative to qualitative. It is also related to other systems for default reasoning. In particular, we show that the work of [Goldszmidt et al., 1990], which applies maximum entropy ideas to --semantics, can be embedded in our framework.