Statistical Foundations for Default Reasoning (1993)by F. Bacchus, A. J. Grove, J. Y. Halpern, and D. Koller
We describe a new approach to default reasoning, based on a principle of 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 phi. The degree of belief can be used to decide whether to defeasibly conclude phi. 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 [GMP90], which applies maximum entropy ideas to epsilon-semantics, can be embedded in our framework.
F. Bacchus, A. J. Grove, J. Y. Halpern, and D. Koller (1993). "Statistical Foundations for Default Reasoning." Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI '93) (pp. 563-569).
author = "F. Bacchus and A. J. Grove and J. Y. Halpern and D.
booktitle = "Proceedings of the Thirteenth International Joint
Conference on Artificial Intelligence (IJCAI '93)",
title = "Statistical Foundations for Default Reasoning",
pages = "563--569",
year = "1993",