The 1987 Workshop on Distributed Artificial Intelligence (DAI) was held at Sea Ranch, California, 3 to 6 December 1987. Twenty-eight participants gathered in this rugged, windswept northern California coastal village to debate the theory and practice of DAI.

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.

Hand's book is well written and well researched. The author has taken great care in presenting previous work in detail and has quoted the erlier literature when applicable. Nevertheless, the book fails in two respects.

The 1987 Workshop on Distributed Artificial Intelligence (DAI) was held at Sea Ranch, California, 3 to 6 December 1987. Twenty-eight participants gathered in this rugged, windswept northern California coastal village to debate the theory and practice of DAI.

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.

Review of Expert Micros

We note with sorrow the passing of Don Waterman, who died on January 4, 1987. Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems").

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered. Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

In order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embodying those principles. The Causal Model Acquisition System (CMACS) learns causal models of physical mechanisms by understanding real-world natural language explanations of those mechanisms. The going Concern Expert ( GCX) uses business and environmental knowledge to assess whether a company will remain in business for at least the following year. The Business Information System (BIS) acquires business and environmental knowledge from in-depth reading of real-world news stories. These systems are based on theories of expert human reasoning and learning, and thus represent steps toward next-generation cognitive expert systems.