Methods for Generating Explanations

A computer program that models an expert in a given domain is more likely to be accepted by experts in that domain, and by nonexperts seeking its advice, if the system can explain its actions. This chapter discusses the general characteristics of explanation capabilities for rule-based systems: what types of explanations they should be able to give, what types of knowledge they will need in order to give these explanations, and how this knowledge might be organized (Figure 18-1). The explanation facility in MYCIN is discussed to illustrate how the various problems can be approached. A consultative rule-based system need not be a psychological model, imitating a human's reasoning process. The important point is that the system and a human expert use the same (or similar) knowledge about the domain to arrive at the same (or similar) answers to a given problem. The system's knowledge base contains the domain-specific knowledge of' an expert as well as facts about a particular problem under consideration. When a rule is used, its actions make changes to the internal data base, which contains the system's decisions or deductions. The process of trying rules and taking actions can be compared to reasoning, and explanations require displays of how the rules use the information provided by the user to make various intermediate deductions and finally to arrive at the answer. If the information contained in these rules adequately shows why an action was taken (without getting into programming details), an explanation can simply entail printing each rule or its free-text translation. This chapter is a revised version of a paper originally appearing in American Journal of Computational Linguistics, Microfiche 62, 1977. The three components of a rule-based system (a rule interpreter, a set of production rules, and a data base) are augmented by an explanation capability. The data base is made up of general facts about the system's domain of expertise, facts that the user enters about a specific problem, and deductions made about the problem by the system's rules. These deductions form the basis of the system's consultative advice. The explanation capability makes use of the system's knowledge base to give the user explanations. This knowledge base is made up of static domain-specific knowledge (both factual and judgmental) and dynamic knowledge specific to a particular problem. Pertbrmance Characteristics of an Explanation Capability The purpose of an explanation capability (EC) is to give the user access as much of the system's knowledge as possible. Ideally, it should be easy for a user to get a complete, understandable answer to any sort of question about the system's knowledge and operation--both in general terms and 340 Methods for Generating Explanations with reference to a particular consultation.