This article presents a dual dependency between AI and programming methodologies. AI is an important source of ideas and tools for building sophisticated support facilities which make possible certain programming methodologies. These advanced programming methodologies in turn can have profound effects upon the methodology of AI research. Both of these dependencies are illustrated by the example of anew experimental programming methodology which is based upon current AI ideas about reasoning, representation and control. The manner in which AI systems are designed, developed and tested can be significantly improved in the programming is supported by a sufficiently powerful partial evaluator. In particular, the process of building levels of interpreters and of intertwining generate and test can be partially automated. Finally speculations about a more direct connection between AI and partial evaluation are presented.

The goal of the Unix Consultant is to provide a natural language help facility that allows new users to learn operating systems conventions in a relatively painless way. UC is not meant to be a substitute for a good operating system command interpreter, but rather, an additional tool at the disposal of the new user, to be used in conjunction with other operating system components.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.

The kind of belief that underlies terms in AI such as'Know!-

"Chunks have long been proposed as a basic organizational unit for human memory. More recently chunks have been used to model human learning on simple perceptual-motor skills. In this paper we describe recent progress in extending chunking to be a general learning mechanism by implementing it within a general problem solver. Using the Soar problem-solving architecture, we take significant steps toward a general problem solver that can learn about all aspects of its behavior. We demonstrate chunking in Soar on three tasks: the Eight Puzzle, Tic-Tat-Toe, and a part of the RI computer-configuration task. Not only is there improvement with practice, but chunking also produces significant transfer of learned behavior, and strategy acquisition."Proceedings of the AAAi-84 National Conference. AAAI, University of Texas at Austin, TX, August, 1984.

AI NEEDS MORE EMF'HASIS ON BASIC RESEARCH Too few people are doing basic research in AT rela-language processing seems misguided to me. There is too tive to the number working on applications The ratio of much emphasis on syntax and not enough on the semantics. This is unfortunate, between existing AI formalisms and English miss the point. Even the applied goals press in English what we already know how to express in proposed by various groups in the U.S., Europe and Japan computerese. Rather we must study those ideas expressible for the next ten years are not just engineering extrapolations in natural language that no-one knows how to represent at from the present state of science.

A fifth answer is also advanced, but is immediately withdrawn. The Innovative Answer: "It also usually means getting fact, there are enough opinions for four men. Roger Schanks, and disagree with the other three. As & hank points out, this is unsatisfactory because it leads Schank hoped that his article would start a debate on to a shifting definition of AI. the issues he raised. Another of these answers, the learning answer, can also What are Schank's four views? Anyone who attempts to clarify a In answer to his question "What is AI all about?", he vague term, like AI, is allowed a certain amount of license in claims to see only two possible answers. The Scientific Answer: "that AI is concerned with highlighting other uses, but there are limits to this license.

The AAAI President's address (the pervious article by Nils Nilsson) presents an eloquent argument for a particular AI paradigm that may be summarized by what Nils calls the "propositional doctrine:" AI is the study of how to acquire and represent knowledge within a logic-like propositional formalism, and the study of how to manipulate this knowledge by use of logical operations and the rule of inference. Although we concur with many of Nil's other assertions, this propositional doctrine seems far to extreme: a lot of interesting and important AI research is done outside of the logic-and theorem- proving paradigm. Indeed, the view that other lines of inquiry serve only to produce tools that may be procedurally attached to an AI (logic-and-theorem-proving ) architecture seems a kind of Logic Imperialism to those of us they wish to relegate to working in the procedure factories. this dismissal of other avenues of research as "not really AI" would normally be cause only for knowing shake of the head and a small chuckle, but when such views are promulgated by the President of the AAAI it is time to take up arms against the logic-and-theorem-proving set -- there is a danger that someone might actually take them seriously! This paper, therefore, constitutes an initial salvo over(into?) their bow. We will focus on two central questions in this rebuttal: What is an appropriate research paradigm for AI? What role should logic-like formal languages and deduction play in the study of AI?

The subject the idea has changed psychology, anthropology, sociology, is attempting to make sense of the world, and often coping and psychiatry should make its pervasiveness and importance with incomplete information, failure to understand, or lacking more evident.

AI research at JPL started in 1972 when design and construction of experimental "Mars Rover" began. Early in that effort, it was recognized that rover planning capabilities were inadequate. Research in planning was begun in 1975, and work on a succession of AI expert systems of steadily increasing power has continued to the present. Within the group, we have concentrated our efforts on expert systems, although work on vision and robotics has continued in a separate organizations, with which we have maintained informal contacts. The thrust of our work has been to build expert systems that can be applied in a real-world environment, and to actually put our systems into such environments, taking a consultative responsibility for meeting user requirements. Several supportive tools for AI are also being built. The current computational environment includes a large main-frame as well as high-performance personal LISP machines. A separate group has been engaged in the design of an intelligent work station with advanced graphic displays intended to interface with AI systems.