Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver. We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.

Reading, Mass.: Addison-Wesley.

Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin's original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin's rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses.

"One of the biggest problems in AT programming is the difficulty of specifying control. Meta-level architecture is a knowledge engineering approach to coping with this difficulty. The key feature of the architecture is a declarative control language that allows one to write partial specifications of program behavior. This flexibility facilitates incremental system dcvclopment and the integration of disparate architectures like demons, object-oriented programming, and controlled deduction. This paper presents the language, describes an appropriate, and cliscusses the issues of compiling. It illustrales the architecture with a variety of examples and reports some experience in using the architecture in building expert systems."Earlier: M. Genesereth and D.E. Smith. Meta-level Architecture. Memo HPP-81-6, Computer Science Department, Stanford University, 1981.In Proceedings of the AAAI, Washington, DC., August, 1983

We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology. The stylized format of ONCOIN's rule has allowed the automatic detection of a number of common errors as the knowledge base has been developed. This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause performance errors.

A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user. This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict. This model partitions an intelligent system into two asynchronous components. The Interviewer's primary function is to gather data while providing an acceptable response time to the user. The Reasoner does most of the symbolic computation for the system. This paper describes the implementation of the model in both timesharing and personal workstation environments, and uses the ONCOCIN system as an example.

I apologise for any mistakes or misinterpretations I may therefore have made. In October 1981,.Japan announced a national project to develop highly innovative computer systems for the 199Os, with the title "Fifth Generation Computer Systems " This paper is a personal view of that project, The fifth generation plan its significance, and reactions to it. In late 1978 the Japanese Ministry of International Trade THIS PAPER PRESENTS a personal view of the Japanese and Industry (MITI) gave ETL the task of defining a project Fifth Generation Computer Systems project.

A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.

We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology. The stylized format of ONCOIN's rule has allowed the automatic detection of a number of common errors as the knowledge base has been developed. This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause performance errors.