Explaining default intuitions using maximum entropy

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.