ABSTRACT
As increasing numbers of knowledge-based systems are developed for operational use, the importance of effective knowledge acquisition becomes more evident. Knowledge acquisition, the process of eliciting and representing (i.e., in computers) expertise from domain experts into a system, consumes the single largest block of development time (Feigenbaum, 1977; Hayes-Roth, Waterman, & Lenat, 1983; McGraw & Harbison-Briggs, 1989). To reduce the time required for this process, numerous tools have been developed that automate or systematize the elicitation and transfer of knowledge from expert to knowledge base. Among these advances are developments in machine learning that enable an expert system to acquire knowledge from “experience” (Michalski, 1980; Michie, 1982); expert system shells that allow domain experts to write their own rules (e.g., MacSMARTS, NEXPERT); and knowledge engineering tools that assist the knowledge engineer (e.g., McGraw & Harbison-Briggs, 1989, chap. 10).
