The Association for the Advancement of Artificial Intelligence held its 1990 Spring Symposium Series on March 27-29 at Stanford University, Stanford, California. This article contains a short summary of seven of the nine symposia that were conducted: AI and Molecular Biology, AI in Medicine, Automated Abduction, Case Based Reasoning, and Knowledge-Based Environments for Teaching and Learning.

Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? (2) If the automated knowledge-acquisition system centers on having an effective cognitive model of the key knowledge worker(s), to what extent does this model account for and attempt to influence human bias in knowledge base rule generation? That is, humans are known to be subject to errors and cognitive biases in their judgment processes. How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized? This article answers these research questions by presenting several prototypical scenes depicting bias and debiasing strategies.

On 15-16 November 1989, I attended the Massachusetts Institute of Technology (MIT) Industrial Liaison Program entitled "Networks and Learning." The topic was neural networks, their power, potential, and promise. A dozen distinguished professors and researchers presented informative and entertaining talks to an audience of technically minded business executives and industrial researchers who subscribe to MIT's popular series of symposia offered through their Industrial Liaison Program. This informal report encapsulates the two-day event with a brief summary of each talk.

The Workshop on Future Directions in NLP was held at Bolt Beranek and Newman, Inc. (BBN), in Cambridge, Massachusetts, from 29 November to 1 December 1989. The workshop was organized and hosted by Madeleine Bates and Ralph Weischedel of the BBN Speech and Natural Language Department and sponsored by BBN's Science Development Program.

This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications.

Thus far, I believe, describing various approximately 120 copies have been limitations of QSIM. At the risk of distributed. The QSIM program is a being scolded again for "employing research tool, not a product, so any universal truths and unarguable commercial rights are retained, and I facts" in support of my position, I cannot warrant that it is free of bugs. Hall examination of the limitations of University of Texas at Austin one's own work is an invaluable Austin, Texas 78712 guide to further research. Akman observes, correctly, that References QSIM is a purely mathematical formalism for expressing qualitative differential Crawford, J.M., Farquhar, A., and Kuipers, 8. 1590 QPC: A Compiler from equation models of the Phvsical Models into Qualitative Differential world, and not a physical modeling Equations In Pr&eedings of the Thank you for publishing our reply Akman's letter refers to his difficulties to Prof. Kuipers in the last issue.

In this issue, Luc Steels takes a new Clay Carr, Homer Chin, Aaron Cohn, overly commercial tone, for example, and insightful look at knowledgebased Michael Compton, Ajit Dingankar, an article that serves mainly to extol systems and provides a synthesis Lance Eliot, David Fogel, Tom the virtues of a commercial product. of several different approaches to Gruber, Uma Gupta, Larry Hall, Jim Second, the article should be well analyzing expertise. It's a long article Hightower, Dwight Johnson, Bob written. We don't have the editorial but, in my opinion, an important Joyce, Murali Krishnamurthi, John staff to do extensive rewriting. I recommend it to anyone with Kunz, Douglas Leyh, Jim MacDonald, and perhaps, unfortunately, an interest in knowledge-level analysis Brigitte Maitre, Robert Newstadt, we rarely publish manuscripts of expert systems. On the same Matthew Realff, Jeff Schlimmer, Allen submitted by non-English-speaking general topic of expert systems but Sherzer, Bob Smith, Scott Staley, Lynn authors.

It (McDermott 1988), and the idea of generic also helps to explicitly focus on how to go tasks and task-specific architectures (Chandrasekaran from the knowledge level to the symbol or 1983). These various proposals are program level. I call this in-between level the obviously related to each other, which makes knowledge-use level. At the knowledge-use it desirable to construct a synthesis that combines level, we focus on issues such as how the their strengths. Such a synthesis is presented overall task will be decomposed into manageable here in the form of a componential subtasks, what ordering will be imposed framework. The framework stresses modularity on the tasks, what kind of access to knowledge and consideration of the pragmatic constraints will be needed (and, consequently, what of the domain.

This article examines the near-term impact of expert system technology on work and the organization. First, an approach is taken for forecasting the likely extent of the diffusion, or success, of the technology. Next, the case of advanced manufacturing technologies and their effects is considered. From this analysis, a framework is constructed for viewing the impact of these technologies -- and technologies in general -- as a function of the technology itself; market realities; and personal, organizational, and societal values and policy choices. Two scenarios are proposed with respect to the application of this framework to expert systems. The first concludes that expert systems will have little impact on the nature of work and the organization. The second scenario posits that expert system diffusion will be pulled by, and will be a contributing factor toward, the evolution of the lean, flexible, knowledge-intensive, postindustrial organization.

In his new book "Alternate Realities: Mathematical Models of Nature and Man (New York: John Wiley and Sons, 1989, 493 pages, $34.95), John L. Casti gives us an impressive, up-to-date look at several areas of mathematics that are being applied to the study of biological and sociological systems.