Abstract
How to enable computers to draw conclusions automatically from bodies of facts has long been recognized as a central problem in artificial-intelligence (AI) research. Any attempt to address this problem requires choosing an application (or type of application), a representation for bodies of facts, and methods for deriving conclusions. This article provides an overview of the issues involved in drawing conclusions by means of deductive inference from bodies of commonsense knowledge represented by logical formulas. We first briefly review the history of this enterprise: its origins, its fall into disfavor, and its recent revival. We show why applications involving certain types of incomplete information resist solution by other techniques, and how supplying domain-specific control information seems to offer a solution to the difficulties that previously led to disillusionment with automatic deduction. Finally, we discuss the relationship of automatic deduction to the new field of “logic programming,’’ and we survey some of the issues that arise in extending automatic-deduction techniques to nonstandard logics.
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