Statistical Foundations for Default Reasoning

We describe a new approach to default reason ing based on a principle of indi erence among possible worlds We interpret default rules as extreme statistical statements thus obtaining a knowledge base KB comprised of statistical and rst order statements We then assign equal probability to all worlds consistent with KB in order to assign a degree of belief to a statement The degree of belief can be used to decide whether to defeasibly conclude Various nat ural patterns of reasoning such as a preference for more speci c defaults indi erence to irrele vant information and the ability to combine in dependent pieces of evidence turn out to follow naturally from this technique Furthermore our approach is not restricted to default reason ing it supports a spectrum of reasoning from quantitative to qualitative It is also related to other systems for default reasoning In par ticular we show that the work of Goldszmidt et al which applies maximum entropy ideas to semantics can be embedded in our framework