Research by members of the Department of Computer Science at Rutgers, and by their collaborators, is organized within the Laboratory for Computer Science research(LCSR). AI and AI-related applications are the major area of research within LCSR, with about forty people-faculty, staff and graduate students-currently involved in various aspects of AI research.

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.

In this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search. Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them.

Work on expert systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We'll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge. I guarantee we'll all return safely, so come along....

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.

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.