Abstract

A substantial portion of the formal work in artificial intelligence over the past decade has involved the formalization of nonmonotonic reasoning. Unfortunately, although much of the original motivation for this work argued that it was necessary for intelligent systems to jump to default conclusions quickly, little of the formal work has justified these arguments: Existing formal theories of nonmonotonic reasoning are nonsemidecidable, with implementations involving repeated calls to theorem provers. We suggest that the computational value of nonmonotonic reasoning lies in its ability to focus the inference process in a hierarchical way. We show that a formal system capable of drawing tentative conclusions without performing a consistency check is capable both of jumping to default conclusions and, perhaps more significantly, of achieving substantial computational savings when a nondefault conclusion is required. 1 INTRODUCTION In all of the applications that have been proposed for no...

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