ECAI, Pisa 1984) to facilitate the decompiling of wealth and power.

During the balmy summer of 1980, about 800 AI researchers pose of the new format, the Committee's recommendation, met on the Stanford campus to hold the first and some expanded ways for members to participate in the AAAI conference. The conference program had no more conference this year. For many of Conference Goals those attendees, it was a special, unique opportunity to have deep colleagial interactions in a very comfortable setting. The most radical change that was considered, but not adopted, was the division of the science and engineering interests into two separate conferences at different times of Even the first national conference, however, was more the year. Many Council members expressed concern that than a gathering of researchers.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.

I want to discuss two aspects of technology transfer. It took a committee several years to come up automation. Then I want to give my two cents worth on with this. It has a lot of words and you can't understand AI as a business activity. Interestingly enough, so does My particular focus is on commercial AI, that is, products Europe. The United States is dead last in utilizing this that incorporate AI that are being sold for profit, as technology-most of which came out of U.S. industry and opposed to "practical" AI, in which AI is incorporated into AI labs. The definition we used at Machine Intelligence views Commercial AI products take the form of equipment, a robot as a computer system with a peripheral attached systems, or software. This forces a different view of a robot, a of demonstrated successes in artificial intelligence systemsmaybe view that is useful in actually thinking about applications.

A variety of proposals for preferred methodological approaches has been advanced in the recent artificial intelligence (AI) literature. Rather than advocating a particular approach, this article attempts to explain the apparent confusion of efforts in the field in terms of differences among underlying methodological perspectives held by practicing researchers. The article presents a review of such perspectives discussed in the existing literature and then considers a descriptive and relatively specific typology of these differing research perspectives. It is argued that researchers should make their methodological orientations explicit when communicating research results, to increase both the quality of research reports and their comprehensibility for other participants in the field. For a reader of the AI literature, an understanding of the various methodological perspectives will be of immediate benefit, giving a framework for understanding and evaluating research reports. In addition, explicit attention to methodological commitments might be a step towards providing a coherent intellectual structure that can be more easily assimilated by newcomers to the field.

The fifth Distributed Artificial Intelligence Workshop was held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984. It was attended by 20 participants from academic and industrial institutions. As in the past, this workshop was designed as an informal meeting. It included brief research reports from individual groups along with general discussion of questions of common interest. This report summarizes the general discussion and contains summaries of group presentations that have been contributed by individual speakers.

The choice of implication as a representation for empirical associations and for deduction as a model of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence.

And even if verification to be accommodated within the SPIV paradigm. But until were possible it would not contribute very much to the such time as we find these learning algorithms (and I development of production software. Hence "verifiability don't think that many would argue that such algorithms must not be allowed to overshadow reliability. Scientists will be available in the foreseeable future) we must face should not confuse mathematical models with reality." the prospect of systems that will need to be modified, in AI is perhaps not so special, it is rather an extreme nontrivial ways, throughout their useful lives. Thus incremental and thus certain of its characteristics are more obvious development will be a constant feature of such than in conventional software applications. Thus the SPIV software and if it is not fully automatic then it will be part methodology may be inappropriate for an even larger class of the human maintenance of the system. I am, of course, of problems than those of AI. not suggesting that the products of say architectural design I have raised all these points not to try to deny the (i.e., buildings) will need a learning capability. Nevertheless, worth of Mostow's ideas and issues concerning the design a final fixed design, that remains "optimal" in a process, but to make the case that such endeavors should dynamically changing world, is a rare event.The similarity also be pursued within a fundamentally incremental and between AI system development and the design of more evolutionary framework for design. The potential of the concrete objects is still present, but it is, in some respects, RUDE paradigm is deserving of more attention than it is rather tenuous I admit.

The founding of a new college at Rutgers in 1969 became the occasion for building a strong computer science presence in the University. Livingston College thus provided the home for the newly organized Department of Computer Science (DCS) and for the beginning of computer science research at Rutgers.

The MITRE Corporation is a scientific and technical an acronym for Knowledge-Based System. Subsequently, organization engaged in system engineering activities, Rome Air Development Center took over support of the principally in support of the United States Air Force and project and continues to fund part of our AI research effort. MITRE is a special kind of engineering MITRE's current research is summarized below. The corporation is a Federal Contract Bedford center is supported by 15 Symbolics Lisp machines Research Center, a designation covering the handful netted to two Vax-780 file servers, while the Washington of independent institutions that perform governmentsponsored center is supported by both a classified and an unclassified research. It is an independent, nonprofit corporation facility, with 2 Lambdas and 2 Symbolics Lisp machines designed and m.anagcd to provide long-term assistance respectively netted to Vax-780 file servers.