KBEmacs is capable of acting as a semiexpert assistant to a person who is writing a program, taking over some parts of the programming task. The abilities of KBEmacs stem directly from a few key AI ideas. However, in many ways KBEmacs does not appear to be an AI system, because its abilities are limited and because (like many applied AI systems) the AI ideas are buried in a large volume of code that has little relevance to AI. The primary goal of this article is to present the AI ideas behind KBEmacs.

John Haugeland's discussion is generally very clear, concrete, accurate, and interesting. My main regret is that he does not take the space to more fully explore certain ideas. John Haugeland's discussion is generally very clear, concrete, accurate, and interesting. My main regret is that he does not take the space to more fully explore certain ideas.

Historically, the evolution of expert systems has been driven by scientifically based fields such as medicine, geology, and computer engineering. More recently, expert system developers have turned their attention to the highly judgmental decision tasks found in business and finance. We introduce the corporate assessment problem, point out the limitations of current expert system approaches to the solution to this problem, and suggest that a more fundamental approach based on recent work in qualitative physics might be fruitful.

The Center for Automation and Intelligent Systems Research at Case Western Reserve University, founded in 1984, provides the setting and the administrative and funding mechanisms for coordinating and focusing the capabilities of faculty members and students from many disciplines and departments to deal with significant realworld problems encountered in the automation of production. The center serves as an interface between separate basic research efforts in the various disciplines and academic departments and the multidisciplinary group efforts needed to deal effectively with nontrivial real problems.

The most widely understood goal of artificial intelligence is to understand and build autonomous, intelligent, thinking machines. A perhaps larger opportunity and complementary goal is to understand and build an interactive knowledge medium.

This article presents a summary of past and current artificial intelligence research within the Computer Science Department at East Texas State University (ETSU). The Computer Science Department at ETSU offers a master of science degree with an emphasis in artificial intelligence. AI research, both past and present, has been funded by a grant from E-Systems, Greenville Division. AI research projects thus far have been implemented in Domain LISP on an Apollo DN300 computer system provided by E-Systems. Other computing facilities available for artificial intelligence research are four workstations, each providing up to 20 users with LISP and PROLOG interpreters. Involved in the research are faculty and students at ETSU and personnel from E-Systems.

AI Magazine Volume 7 Number 1 (1986) ( AAAI) of the crucial terms involved in his analysis, such as "probability" Mauadne 4(4):7-14 falsity of his claims is often impossible to assess. Nute, Donald k. '(1980) Topics in conditional logic Dordrecht, Holland: conceptions upon which his view is based do indeed conform M. Ringle, (Ed.), Philosophical Perspectives in Artificial Intelligence traditional conceptions should not be taken for granted, Terry L. Rankin his observation that "Probability theory is today our primary At hens, Georgia such as "average" and "likely," and therefore it is the most natural language for describing those aspects of (heuristic) performance that we seek to improve" (p. Artificial Intelligence and Robotics: On general theoretical grounds, I think, there are excellent Five Overviews. Busi-reasons to suppose that (a)-(f) are fundamental ness/Technology Books; 1984. Gevarter's work was published by the National that serious difficulties seem to confront the theoretical Bureau of Standards as a set of five volumes, and this book, framework he apparently endorses, where these difficulties published by Business/Technology Books, is Gevarter's are especially severe from an epistemological perspective.

Twenty-five years ago I had a dream, a daydream, if you will. A dream shared with many of you. I dreamed of a special kind of computer, which had eyes and ears and arms and legs, in addition to its "brain." I did not dream that this new computer friend would be a means of making money for me or my employer or a help for my country - though I loved my country then and still do, and I have no objection to making money. I did not even dream of such a worthy cause as helping the poor and handicapped of the world using this marvelous new machine. No, my dream was filled with the wild excitement of seeing a machine act like a human being, at least in many ways.

The Knowledge-Based Editor in Emacs (KBEmacs) is the current demonstration system implemented as part of the Programmer's Apprentice project. KBEmacs is capable of acting as a semiexpert assistant to a person who is writing a program, taking over some parts of the programming task. The abilities of KBEmacs stem directly from a few key AI ideas. However, in many ways KBEmacs does not appear to be an AI system, because its abilities are limited and because (like many applied AI systems) the AI ideas are buried in a large volume of code that has little relevance to AI. The primary goal of this article is to present the AI ideas behind KBEmacs. In addition, the construction of applied AI systems is discussed, in general, using the development of KBEmacs as a case history