Abstract. This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.

Models of Category Learning Pat Langley 1 (langley@isle.org) Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, CA 94306 USA Abstract In this paper, we compare the rhetoric that sometimes appears in the literature on computational models of category learning with the growing evidence that different theoretical paradigms typically produce similar results. In response, we suggest that concrete computational models, which currently dominate the field, may be less useful than simulations that operate at a more abstract level. We illustrate this point with an abstract simulation that explains a challenging phenomenon in the area of category learning -- the effect of consistent contrasts -- and we conclude with some general observations about such abstract models. Introduction and Overview Learning is one of the ubiquitous aspects of human behavior, so it seems natural that the process of learning has drawn significant attention within both ...