Why People Think Computers Can't MOST PEOPLE ARE CONVINCED computers cannot think. I think those specialists are too used t,o That is, really think. This leads them to believe that there can't "thinking." This essay explains why they are wrong . Can Computers Do Only What They're Told? concerned with huge numerical computations: that's why the things were called computers. Most people think that "creativity" Yet even then a fringe of people envisioned what's now If so, then no computer can create-since, clearly, they realized that computers could manipulate not only numbers anything machines can do can be explained. To see what's wrong with that, we'd better turn aside able to go beyond arithmetic, perhaps to imitate the informa-from those outstanding works our cuhure views as very best Con processes that happen inside minds.

I apologise for any mistakes or misinterpretations I may therefore have made. In October 1981,.Japan announced a national project to develop highly innovative computer systems for the 199Os, with the title "Fifth Generation Computer Systems " This paper is a personal view of that project, The fifth generation plan its significance, and reactions to it. In late 1978 the Japanese Ministry of International Trade THIS PAPER PRESENTS a personal view of the Japanese and Industry (MITI) gave ETL the task of defining a project Fifth Generation Computer Systems project.

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user. This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict. This model partitions an intelligent system into two asynchronous components. The Interviewer's primary function is to gather data while providing an acceptable response time to the user. The Reasoner does most of the symbolic computation for the system. This paper describes the implementation of the model in both timesharing and personal workstation environments, and uses the ONCOCIN system as an example.

A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.

The acquisition of expert knowledge is fundamental to the certain of expert systems. The conventional approach to building expert systems assumes that the knowledge exists, and that it is feasible to find an expert who has the knowledge and can articulate it in collaboration with a knowledge engineer. This article considers the practice of knowledge engineering when these assumptions can not be strictly justified. It draws on our experiences in the design of VLSI design methods, and in the prototyping of an expert assistant for VLSI design. We suggest methods for expanding the practice of knowledge engineering when applied to fields that are fragmented and undergoing rapid evolution. We outline how the expanded practice can shape and accelerate the process of knowledge generation and refinement. Our examples also clarify some of the unarticulated present practice of knowledge engineering.

BBN's project in knowledge representation for natural language understanding is developing techniques for computer assistance to decision maker who is collecting information about and making choices in a complex situation. In particular, we are designing a system for natural language control of an intelligent graphics display. This system is intended for use in situation assessment and information management.

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....

Two closely related aspects of artificial intelligence that have received comparatively little attention in the recent literature are research methodology, and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant as Artificial Intelligence matures into a science and spins off major application efforts. It is imperative to analyze the repertoire of AI methods with respect to past experience, utility in new domains, extensibility, and functional equivalence with other techniques, if AI is to become more effective in building upon prior results rather than continually reinventing the proverbial wheel. Similarly, awareness of research methodology issues can help plan future research buy learning from past successes and failures. We view the study of research methodology to be similar to the analysis of operational AI techniques, but at a meta-level ; that is, research methodology analyzes the techniques and methods used by the researchers themselves, rather than their programs, to resolve issues of selecting interesting and tractable problems to investigate, and of deciding how to proceed with their investigations. A public articulation of methodological issues that typically remain implicit in the literature may provide some helpful orientation for new researchers and broaden the perspective of many AI practitioners.

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.