The First International Workshop on User Modeling in Natural Language Dialogue Systems was held 30-31 August 1986 in Maria Laach, West Germany. Issues addressed by the participants included the appropriate contents of a user model, techniques for constructing user models in both understanding and generating natural language dialogue, and the development of general user-modeling systems. This article includes an overview of the presentations made at the workshop. It is a compilation of the author's impressions and observations and is, therefore, undoubtedly incomplete; and at times might fail to accurately represent the views of the researcher presenting the work.

Presented is a discussion of several issues raised during the workshop sponsored by the Association for the Advancement of Artificial Intelligence on Coupling Symbolic and Numeric Computing in Expert Systems, which was held on 27 to 29 August 1987 in Seattle, Washington. Issues include the definition of coupled systems, motivations for coupling, coupled system architectures, and key factors in the design of coupled systems.

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.

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.

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.

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.

Does the United States have a 51st state called Australia? A superficial look at the artificial intelligence (AI) research being done here could give that impression. A look beneath the surface, though, indicates some fundamental differences and reveals a dynamic and rapidly expanding AI community. General awareness of the Australian AI research community has been growing slowly for some time. AI was once considered a bit esoteric -- the domain of an almost lunatic fringe- but the large government -backed programs overseas, as well as an appreciation of the significance of AI products and potential impact on the community, have led to a reassessment of this image and to concerted attempt to discover how Australia is to contribute to the world AI research effort and hoe the country is to benefit from it. What we have seen as result is not an incremental creep of AI awareness in Australia but a quantum leap with significant industry and government support. The first systematic study of the Australian AI effort was undertaken by the Australian Department of Science (DOS) in 1986. The study took as its base the long-running research report Artificial Intelligence in Australia (AIIA), produced by John Debenham (1986). The picture that emerged is interesting. AI researchers are well qualified, undertaking research at the leading edge in their fields, and have significant potential to develop further. The results of this study were published by DOS in the Handbook of Research and Researchers in Artificial Intelligence in Australia (Department of Science1986). This article is based on key findings from the study and on additional information gained through meeting and talking with researchers and research groups.