Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
The mathematics of inheritance systems
The mathematics of inheritance systems
Readings in nonmonotonic reasoning
General theory of cumulative inference
Proceedings of the 2nd international workshop on Non-monotonic reasoning
Benchmark problems for formal nonmonotonic reasoning
Proceedings of the 2nd international workshop on Non-monotonic reasoning
A skeptical theory of inheritance in nonmonotonic semantic networks
Artificial Intelligence
A note on the inevitability of maximum entropy
International Journal of Approximate Reasoning
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Theoretical foundations for non-monotonic reasoning in expert systems
Logics and models of concurrent systems
Notes on “A clash of intuitions”
Artificial Intelligence
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
What does a conditional knowledge base entail?
Artificial Intelligence
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Entropy and Variable Strength Defaults
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Logically Sound Method for Uncertain Reasoning with Quantified Conditionals
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Extending the Maximum Entropy Approach to Variable Strength Defaults
Annals of Mathematics and Artificial Intelligence
Belief functions and default reasoning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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While research into default reasoning is extensive and many default intuitions are commonly held, no one system has yet captured all these intuitions nor given a formal account to motivate them. This paper argues that the extended maximum entropy approach which incorporates variable strength defaults provides a benchmark for default reasoning that is not only objectively motivated but also satisfies all the accepted default intuitions. It is shown that the behaviour of the approach coincides with a wide range of default intuitions taken from examples in the literature, and can be used to explain why some examples have led to confusion. Moreover, analysing the solutions produced by the maximum entropy approach enables clearer differentiation between the default knowledge they contain and the default inferences required of the reasoning system. This suggests that the maximum entropy approach can be used as a benchmark both for eliciting default knowledge when building a knowledge base and, by comparison, for clarifying the underlying biases of other default reasoning systems.