The Fourth Uncertainty in Artificial Intelligence workshop was held 19-21 August 1988. The workshop featured significant developments in application of theories of representation and reasoning under uncertainty. A recurring idea at the workshop was the need to examine uncertainty calculi in the context of choosing representation, inference, and control methodologies. The effectiveness of these choices in AI systems tends to be best considered in terms of specific problem areas. These areas include automated planning, temporal reasoning, computer vision, medical diagnosis, fault detection, text analysis, distributed systems, and behavior of nonlinear systems. Influence diagrams are emerging as a unifying representation, enabling tool development. Interest and results in uncertainty in AI are growing beyond the capacity of a workshop format.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time. Finally, there are several areas -- cooperating systems and terrain reasoning, for example -- where, given some impetus, AI might be able to provide help in the near future.

Sometimes less ambiguous, are extremely complex.

This is a summary of the Workshop on Planning that was sponsored by the Defense Advanced Research Project Agency and held in Santa Cruz, California, on October 21-23, 1987. The purpose of this workshop was to identify and explore new directions for research in planning.

We offer a simple mechanism, that of metalingual definition (D. G. Hays, The Meaning of a Term is a Function of the Theory in Which It Occurs, SIGLASH Newsletter, 1973, 6, 4:S-11).

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 report contains a historical perspective on previous Distributed Artificial Intelligence Workshops, highlights of the roundtable discussions, and a collection of research abstracts submitted by the participants.

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.

Initial Development Approach In the following eight subsections, we present a brief discussion of methodology for expert system development, selection of problem and tools, knowledge engineering and prototype implementation, operational feasibility, and the actual development of a working prototype of a process planning expert system. Methodology for Expert System Development Expert systems require a software development methodology that differs in some respects from those methodologies used for conventional systems. Most knowledge-based development methodologies used by organizations experienced in building expert systems are similar in that they concentrate on the early (feasibility) stages of a project. Very little has been published on the later stages, which are concerned with expert system delivery, integration, and maintenance. During the development of OPGEN, we incorporated the lessons learned in these early stages and revised our original approach to provide for integration and maintenance. Most expert system development methodologies are a variation on the following theme, which paraphrases Haycs-Roth (1985): (1) expert system technology is determined to be relevant to a product; (2) management provides an opportunity for action; (3) a preliminary business application is assembled; (4) a knowledge engineering consultant verifies the opportunity; (5) a knowledge engineering project team is formed and assesses the knowledge; (6) the knowledge engineering project manager plans the project; (7) the user organization Figure 2 OPGEN bzput Circuit Layout Diagram.