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.

Public opinion about artificial intelligence is schizophrenic. "It will never work" versus "It might cost me changes that are daily taking place in the This dichotomy of attitudes reflects a collective world because we read about them and know confusion about AI.

The Advanced Computational Methods Center (ACMC) established at the University of Georgia in 1984, supports several research projects in artificial intelligence. The primary goal of AI research at ACMC is the design and installation of a logic-programming environment with advanced natural language processing and knowledge-acquisition capabilities on the university's highly parallel CYBERPLUS system from Control Data Corporation. This article briefly describes current research projects in artificial intelligence at ACMC

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.

Granting all of this, the only complaint that schema constrains mental ascriptions once a system might be raised is altogether excusable, if not also entirely is specified; but it puts no limit on which systems should minor, i.e., that the material presented might not be so have mental states ascribed to them." "Supertrap" which strikes matches in the presence of gassoaked mice, topples dictionaries on mice, and, of course, Discursively considered, however, and especially for snaps shut whenever mice nibble its bait "These habits the purposes of AI research, these very same strengths can betray a common malevolent thread, which is generalizable be seen as weaknesses from the viewpoint of at least two by (and only by) ascribing a persistent goal: dead mice." Now it clearly aside, ascription is important for AI because it provides was not Pearlis aim to forestall alternative theories or to one more way to detect patterns that might otherwise go justify his own approach in contrast to ...