Good books, well conceived, well written, and well presented, can do much to promote the science of AI and the AAAI organization. The AAAI Press edited collections, from which the articles of this issue are excerpted, are designed to reach out to an audience that wants to learn more about AAAI and AI.

"Organizations today Intelligence concluded its national "This was the fictional characters feelings and attitudes Ebby Adhami of Ernst & Young/UK first AI art exhibition and the first and goals with which "readers" presented National Westminster time this many mobile robots have can interact. Bank's PHAROS program that details ever been together. The invited talks Learning provided a major theme European marketing possibilities for made some exciting new connections. Introduced less I was especially excited by Christopher speaker Oliver Selfridge celebrating than a year ago, it has 135,000 users Langton's survey of'Artificial Life' "the joy of why we're here: to start to currently. It is an example of AI as and Dana Ballard's new approach to understand the mysteries of the marketing tool, he noted, opening a visual perception. The question for us," whole new communications channel showed that machine translation is he declared, "is to know who we are, for the bank. It was, typically, an AI really getting somewhere, and Andrew what makes us grow, think, feel. Other talks included task than knowing the physical knew only that he had a problem, Lawrence Hunter's "AI and Molecular world we live in-and key to the not that AI was the answer.

The interdisciplinary makeup allowed for an expansion of the scope of Glymour's One notable extension was the move from android epistemology to android ethics. "they can know everything we know Margaret Boden presented her work Hayes and Ford were responding Participation was limited to 40 If the first two workshops on to the debate in Scientific American researchers selected from several disciplines human and machine cognition are (January 1990) between Searle and (principally computer science, representative, these meetings will the Churchlands about whether a philosophy, and psychology); become hotbeds of constructive and machine could think. Ironically, although this approach makes for much-needed debate. They focus on from the perspective of Hayes and stimulating discussion, it has resulted the foundational and methodological Ford, Searle and the Churchlands are in a competitive review process concerns of those who want to forge essentially in agreement, diverging (about a 10-percent acceptance rate). It is just a theories about the necessary in U.S. politics, the theme of the fact of life that there isn't much material basis (biological versus parallel) Second International Workshop on agreement about methodology and for intelligence. They both Human and Machine Cognition was, foundational issues within these two make specific implementation features What do androids know, and when fields. The positions covered One feature of the workshop that for intelligence. As might be expected, a wide range: "They can know facilitated and, at times, obstructed Paul Churchland objected to this only what androids can know: Android fruitful discussion was its highly interdisciplinary grouping.

Industrial managers, engineers, and technologists have many expectations from artificial intelligence and its application to knowledge-based systems. Although the past decade has witnessed a number of innovative applications of AI in manufacturing, the field is still in its infancy and holds even greater promise for the future. The AAAI Press book Artificial Intelligence Applications in Manufacturing, (from which the following article was selected) presents a number of articles that relate to the enhancement of planning and decision making capabilities in today's automated production environments.

Allen Newell, one of the founders of AI and cognitive science, died on July 19th, 1992.

AI Review

The work described in my Ph.D. dissertation (Fischer 1991)1 merges computational and cognitive investigations of abductive reasoning. It is the outcome of seven years of research focusing on abductive explanation generation and involving the departments of computer and information science, industrial and systems engineering, pathology, and allied medical professions at The Ohio State University.

The Department of Computer Science at Louisiana State University (LSU) has been involved in robotics research since 1992 when the Robotics Research Laboratory (RRL) was established as a research and teaching program specializing in autonomous mobile robots (AMRS). Researchers at RRL are conducting high-quality research in amrs with the goal of identifying the computational problems and the types of knowledge that are fundamental to the design and implementation of autonomous mobile robotic systems. In this article, we overview the projects that are currently under way at LSU's RRL.

Recent attempts to develop larger and more complex knowledge-based systems have revealed the shortcomings and problems of centralized, single-agent architectures and have acted as a springboard for research in distributed AI (DAI). Although initial research efforts in DAI concentrated on issues relating to homogeneous systems (that is, systems using agents of a similar type or with similar knowledge), there is now increasing interest in systems comprised of heterogeneous components. The workshop on cooperation among heterogeneous intelligent agents, held July 15 during the 1991 National Conference on Artificial Intelligence, was organized by Evangelos Simoudis, Mark Adler, Michael Huhns, and Edmund Durfee. It was designed to bring together researchers and practitioners who are studying how to enable a heterogeneous collection of independent intelligent systems to cooperate in solving problems that require their combined abilities.

We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. However, we are already beginning to see how to mesh plan execution with plan generation and learning.