Formulation of Expert System Knowledge

Abstract

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.