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.

Ms. Claudia Mazzetti AAAI AAAI has supported small workshops for the last several years. This support has 445 Burgess Drive included publicity, printing, office help, and subsidies for other expenses. Any topic in AI science or technology is appropriate, and anyone may volunteer Submit all proposals to: to organize a workshop on any topic. The organizer(s) should determine Jay M. Tenenbaum, Chair, AAAI Conference the topic, the date, the site, and the procedure for selecting papers and attendees. Committee He or she should also decide whether preprints should be distributed.

Tin Nguyen performed the work contained in the article "Knowledge Base Verification" while at Lockheed and is currently working for Bell Northern Research as a member of the research Deanne Pecora, a staff engineer with the Lockheed Artificial Intelligence Center, 2710 Sand Hill Road, Menlo Park, California 94025, is working on Rick Briggs, author of "Knowledge Representation and Inference in Sanskrit: A applying knowledge-based systems to Review of the First National Conference," is a senior engineer at Delfin Systems, real problems. She is a coauthor of 1349 Moffett Park Drive, Sunnyvale, California 94089. Briggs is currently working "Knowledge Base Verification." Walt Perkins, coauthor of IIKnowledge Base Verification" is a consulting scientist Lindley Darden, who wrote "Viewing the History of Science as Compiled Hindsight,lI with the Lockheed Artificial is an associate professor in the departments of philosophy and history and InteIligence Center, 2710 Sand Hill a member of the graduate faculty in the Committee on the History and Philosophy Road, Menlo Park, California 94025 of Science at the University of Maryland, College Park. She is currently and the principal developer of the serving in the second year of a halftime research appointment at the University Lockheed expert system. of Maryland Institute for Advanced Computer Studies.

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

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.

An important issue in achieving acceptance of computer systems used by the nonprogramming community is the ability to communicate with these systems in natural language. Often, a great deal of time in the design of any such system is devoted to the natural language front end. An obvious way to simplify this task is to provide a portable natural language front-end tool or facility that is sophisticated enough to allow for a reasonable variety of input; allows modification; and, yet, is easy to use. It allows for user input to be in sentence or nonsentence form or both, provides a detailed parse tree that the user can access, and also provides the facility to generate responses and save information.

This support has in-Intelligence will be held 13-17 July 1987 in M. Tenenbaum, Chair; Ronald Brachman,:luded publicity, printing, office help, and Seattle, Washington. Typical grants AAAI-87's Technical Program will from the membership for conference iave been $5,000, although requests for up present outstanding research papers in AI. sites for 1988, 1990, and 1991. The proposal to $10,000 will be considered. Any topic in These papers will be divided into those emphasizing should be structured around the new AI science or technology is appropriate, basic research and those emphasizing five day format described elsewhere in this and anyone may volunteer to organize a applied research. Based on a predictive workshop on any topic.

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.