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 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.

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.

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.

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.

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.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.