This article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty. We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution. Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the apporiate direction. Once constructed, the relationships can easily be reserved into the less intuitive direction in order to perform inference inference and diagnosis. In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation.

The questions of how humans process uncertain information is important to the development of knowledge-based systems in term of both knowledge acquisition and knowledge representation. This article reviews three bodies of psychological research that address this question: human perception, human probabilistic and statistical judgement, and human choice behavior. The general conclusion is that human behavior under certainty is often suboptimal and sometimes even fallacious. Suggestions for knowledge engineers in detecting and obviating such errors are discussed. The requirements for a system designed to reduce the effects of human factors in the processing of uncertain knowledge are introduced.

This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.

This article is a written version of an invited talk on artificial intelligence (AI) and the history of science that was presented at the Fifth National Conference on Artificial Intelligence (AAAI-86) in Philadelphia on 13 August 1986. Included is an expanded section on the concept of an abstraction in AI; this section responds to issues that were raised in the discussion which followed the oral presentation. The main point here is that the history of science can be used as a source for constructing abstract theory types to aid in solving recurring problem types. Two theory types that aid in forming hypotheses to solve adaptation problems are discussed: selection theories and instructive theories. Providing cases from which to construct theory types is one way in which to view the history of science as "complied hindsight" and might prove useful to those in AI concerned with scientific knowledge and reasoning.

This conference is analogous to the ancient texts but little procedural consultation of philosophers and cognitive information), we had to rely on the This report is a review of the First psychologists by computer scientists pandits to whom the oral tradition had National Conference on Knowledge in the beginnings of AI. been passed. Representation and Inference in Western psychology and philosophy is The conference was inspired by Sri Sanskrit, Bangalore, India, 20 through quite different from the Indo-Aryan Paramananda Bharathi Swamiji and 22 December, 1986 The conference tradition: the former has its basis in was organized by Dr. H. N. Mahabala was inspired by an article that Aristotelian logic and the scientific (president, Computer Society of India; appeared in the Spring 1985 issue of method, whereas the latter is also chairman, Indian Institute of AI Magazine--"Knowledge based on introspection and internal Technology) and others. The conference Representation in Sanskrit and experience Nevertheless, both these was attended by the vice-chairman Artificial Intelligence." Virtually text.The purpose of AI in this context every institute of science, mathematics is to derive a "method" for natural language and engineering was represented. A working group has been created to was implicit; it was not the focus.

Even though sues raised during the workshop sponsored emerged during the workshop. In many situations, users are not sufficiently defined or Seattle, Washington. Issues include the need guidance and counseling in order understood to be amenable to traditional definition of coupled systems, motivations to solve the problem at hand. In control system--one that combines such situations, users often need help techniques from artificial intelligence in determining which specific algorithm (AI), control theory, and operations or technique should be research (Kowalik et al. 1986). In other situations, traditional techniques to perform the need is more basic--for guidance in many routine tasks, sophisticated determining whether the problem at hand can be solved and, if so, whether techniques are needed to handle many the resources that can be brought to of the humanlike functions.

We describe a computer program that implements an algorithm to verify the consistency and completeness of knowledge bases built for the Lockheed expert system (LES) shell. The algorithms described here are not specific to this particular shell and can be applied to many rule-based systems. The computer program, which we call CHECK, combines logical principles as well as specific information about the knowledge representation formalism of LES. The program checks both goal-driven and data-driven rules. CHECK identifies inconsistencies in the knowledge base by looking for redundant rules, conflicting rules, subsumed rules, unnecessary IF conditions, and circular rule chains. Checking for completeness is done by looking for unreferenced attribute values, illegal attribute values, dead-end IF conditions, dead-end goals and unreachable conclusions. These conditions can be used to suggest missing rules and gaps in the knowledge base. The program also generates a chart that shows the dependencies among the rules. CHECK can help the knowledge engineer detect many programming errors even before the knowledge base testing phase. It also helps detect gaps in the knowledge base testing phase. It also helps detect gaps in the knowledge base that the knowledge engineer and the expert have overlooked. A wide variety of knowledge bases have been analyzed using CHECK.

This article discusses several effective techniques for expert system knowledge acquisition based on the techniques that were successfully used to develop the Central Office Maintenance Printout Analysis and Suggestion System (COMPASS). Knowledge acquisition is not a science, and expert system developers and experts must tailor their methodologies to fit their situation and the people involved. Developers of future expert systems should find a description of proven knowledge-acquisition techniques and an account of the experience of the COMPASS project in applying these techniques to be useful in developing their own knowledge-acquisition procedures.

The first step in the development of an expert system is the extraction and characterization of the knowledge and skills of an expert. This step is widely regarded as the major bottleneck in the system development process. To assist knowledge engineers and others who might be interested in the development of an expert system, I offer (1) a working classification of methods for extracting an expert's knowledge, (2) some ideas about the types of data that the methods yield, and (3) a set of criteria by which the methods can be compared relative to the needs of the system developer. The discussion highlights certain issues, including the contrast between the empirical approach taken by experimental psychologists and the formalism-oriented approach that is generally taken by cognitive scientists.

Turbine Generator Diagnostics," see "Research in Artificial Intelligence at Osborne, Robert L. Tenenbaum, Jay M., see Pan, Jeff. the University of Pennsylvania."