Statistical Foundations for Default Reasoning

We describe a new approach to default, reason­ ing, based on a principle oi 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 state­ ment φ. The degree of belief can be used to de­ cide whether to defeasibly conclude φ. Various natural patterns of reasoning, such as a prefer­ ence for more specific defaults, indifference to irrelevant information, and the ability to com­ bine independent pieces of evidence, turn out to follow naturally from this technique. Further­ more, our approach is not restricted to default reasoning; it supports a spectrum of reasoning. , from quantitative to qualitative. It is also re­ lated 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.

Authors

• Fahiem Bacchus
• Adam J. Grove Dale Schuurmans
• Joseph Y. Halpern
• Daphne Koller

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