As a communication scholar, I am This latest computer revolution well aware that many traditionalists has taken shape only within the view the respective disciplines of past five years. My recently completed These two revolutions have been master's thesis argues against this operating independently with limited view. Many concepts from the field success, instead of together with The workshops on Artificial Intelligence of communication have been used by potentially phenomenal success. The and Statistics have broadened the flow artificial intelligence researchers and multimedia revolution has successfully of information between the two fields scholars in the development of AI. broken into the marketplace on and encouraged interdisciplinary work. The central argument of my perspective all levels, but lacks the key component General Chair: R.W. Oldford (U. is that artificial intelligence is (symbolic reasoning) needed for Waterloo); man Program Chair: P. Cheese Sponsers: Sot. for A.I. and potential to provide the current multimedia By transcending traditional Stats., Int'l Ass. for Stat.

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.

My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.

The Fourth International Symposium on Artificial Intelligence (ISAI) was held in Cancun, Mexico, 13-15 November 1991. What, another international AI conference, you say? The first symposium was held in 1988. This fourth consecutive annual conference drew the participation of visitors from several international AI communities, including the United States, Mexico, Canada, Germany, Japan, England, France, Italy, The Netherlands, Spain, China, Belgium, Australia, and Singapore -- an impressive breadth of participants for a conference that has existed for only four years.

The Quest for the Thinking Computer

I agree with much of what Dr. Levinson has to say, especially