MOLE: A Knowledge-Acquisition Tool for Cover-and-Differentiate Systems

By distinguishing between covering and differentiating knowledge and by using this distinction to help it refine the expert’s preferences, MOLE is able to disambiguate an under-specified knowledge base and to interactively refine an incomplete knowledge base.

Abstract

MOLE is a knowledge-acquisition tool for generating expert systems that do heuristic classification. More specifically, MOLE assumes that the task can be performed using a cover-and-differentiate problem-solving method. Using this method, the expert system generated by MOLE proposes a set of candidate explanations for the events or states that need to be explained (or covered) and then differentiates among the candidates, picking the candidates that best explain the specified events or states. The problem-solving method presupposed by MOLE makes several heuristic assumptions about the space of covering hypotheses that MOLE is able to exploit when acquiring knowledge. In particular, by distinguishing between covering and differentiating knowledge and by using this distinction to help it refine the expert’s preferences, MOLE is able to disambiguate an under-specified knowledge base and to interactively refine an incomplete knowledge base.