John was best known lately for his work on expert systems, for conversations that helped calibrate my mental compass. He was enthusiastic about and welcomed the added strength that he gave me. Without John, our laboratory is noticeably less than it achieve something truly important. We are proud to have been his colleagues and fortunate John's attitude about my counterarguments was that they I wish he were still here to overcome them.

Artificial intelligence is that part of computer science that concerns itself with the concepts and methods of symbolic inference and symbolic representation of knowledge. Its point of departure -- it's most fundamental concept -- is what Newell and Simon called (in their Turing Award Lecture) "the physical symbol system." But within the last fifteen years, it has concerned itself also with signals -- with the interpretation or understanding of signal data. AI researchers have discussed "signal-to symbol transformations," and their programs have shown how appropriate use of symbolic manipulations can be of great use in making signal processing more effective and efficient. Indeed, the programs for signal understanding have been fruitful, powerful, and among the most widely recognized of AI's achievements.

I went to a panel on "Education in AI" and stepped back into an argument that I had thought settled several years ago. The debate was between the "scruffies," led by Roger Schank and Ed Feignbaum, and the "neats," led by Nils Nilsson. The neats argued that no education in AI was complete without a strong theoretical component, containing, for instance, courses on predicate logic and automata theory. The scruffies maintained that such a theoretical component was not only unnecessary, but harmful.

The second KL-One workshop was held over a five-day period in October, 1981. The workshop included a general conference session, wherein people could report on activies at their own institutions, and a two day working research session.

In this respect, AI is analogous to applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the context being described by these languages. In these areas AI is concerned with content as well as form. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.

Minutes of the Second Annual Meeting of the American Association for Artificial Intelligence Abstract Summary of the Executive Council meeting held 26 August 1981 in Vancouver, British Columbia. Summary of the Executive Council meeting held 26 August 1981 in Vancouver, British Columbia.

Our program in AI is part of the larger departmental focal area of cybernetics which integrates both AI and brain theory (BT). Our research also draws upon a new and expanding interdepartmental program in cognitive science that brings together researchers in cybernetics, linguistics, philosophy, and psychology. This interdisciplinary approach to AI has already led to a number of fruitful collaborations in the areas of cooperative computation, learning, natural language parsing, and vision.

A funny thing happened to me at IJCAI-81. I went to a panel on "Education in AI" and stepped back into an argument that I had thought settled several years ago. The debate was between the "scruffies," led by Roger Schank and Ed Feignbaum, and the "neats," led by Nils Nilsson. The neats argued that no education in AI was complete without a strong theoretical component, containing, for instance, courses on predicate logic and automata theory. The scruffies maintained that such a theoretical component was not only unnecessary, but harmful.

This paper presents the view that artificial intelligence (AI) is primarily concerned with propositional languages for representing knowledge and with techniques for manipulating these representations. In this respect, AI is analogous to applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the context being described by these languages. Notable exceptions involve "commonsense" knowledge about the everyday would ( no other specialty claims this subject area as its own ), and metaknowledge (or knowledge about the properties itself). In these areas AI is concerned with content as well as form. We also observe that the technology that seems to underly peripheral sensory and motor activities (analogous to low-level animal or human vision and muscle control) seems to be quite different from the technology that seems to underly cognitive reasoning and problem solving. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.

This issue of AI Magazine is the first for which I extent with the Newsletter, particularly with respect to have the privilege and the responsibility of serving as technical articles, which one now finds in both Editor. Lee Erman (whom you can blame for this event) publications. As the magazine matures, however, I asked me to serve as Editor under the assumptions that I expect to see it develop its own distinctive style. We'll was interested in the dissemination of interesting and see. A condition under which I accepted Lee's offer was having just left the hectic world of research management that I could enlist a group of Associate Editors who could at ARPA, I would have copious amounts of free time on help dig out interesting and informative material, and my hands (false). I accepted the offer mainly because it's keep the magazine broad in scope.