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.

While some proposals for supercomputers increase the powers of existing machines like CDC and Cray supercomputers, others suggest radical changes of architecture to speed up non-traditional operations such as logical inference in PROLOG, recognition/ action in production systems, or message passing. We examine the case of parallel PROLOG to identify several related computations which subsume those of parallel PROLOG, but which have much wider interest, and which may have roughly the same difficulty of mechanization. Similar considerations apply to some other proposed architectures as well, raising the possibility that current efforts may be limiting their aims unnecessarily.

Since the 1970's AI research has become very active in China and certain results have been achieved. This paper is intended to review briefly what was and is going on in AI field in China.

I completely agree with this evaluation book describes the current state of work in artificial intelligence The book contains descriptions of the "knowledge engineer" (AI) in the US and abroad, it outlines the history of

Cooperative distributed problem solving networks are distributed networks of semi-autonomous processing nodes that work together to solve a single problem. The Distributed Vehicle Monitoring Testbed is a flexible and fully-instrumented research tool for empirically evaluating alternative designs for these networks. The testbed simulates a class of a distributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. There are two important aspects to the testbed: (1.) it implements a novel generic architecture for distributed problems solving networks that exploits the use of sophisticated local node control and meta-level control to improve global coherence in network problem solving; (2.) it serves as an example of how a testbed can be engineered to permit the empirical exploration of design issues in knowledge AI systems. The testbed is capable of simulating different degrees of sophistication in problem solving knowledge and focus-of attention mechanisms, for varying the distribution and characteristics of error in its (simulated) input data, and for measuring the progress of problem solving. Node configuration and communication channel characteristics can also be independently varied in the simulated network.

This issue of the AI Magazine initiates a new and artistic efforts can have a real effect on our and (we hope) regular feature, Reviews of Books. Before presenting our first book review, a few comments Visions of applications of computer technology can about the aims of this feature are in order. However, we are general public. For the reasons outlined above as particularly interested in reviewing publications well as others, review and discussion of popular that attempt to provide tutorial and other forms treatments of work in AI are a useful adjunct to of summary discussions of broad areas of artificial the standard sorts of review to be included in this intelligence, publications that examine existing research column. We extend an invitation to anyone interested since one goal of the AI Magazine is to provide a in submitting a review.

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques.

GLISP is a high-level language that is complied into LISP. It provides a versatile abstract-data-type facility with hierarchical inheritance of properties and object-centered programming. GLISP programs are shorter and more readable than equivalent LISP programs. The object code produced by GLISP is optimized, making it about as efficient as handwritten Lisp. An integrated programming environment is provided, including automatic incremental compilation, interpretive programming features, and an intelligent display-based inspector/editor for data and data-type descriptions. GLISP code is relatively portable; the compiler and data inspector are implemented for most major dialects of LISP and are available free or at nominal cost.

Rational psychology is the conceptual investigation of psychology by means of the most fit mathematical concepts. Several practical benefits should accrue from its recognition.

Early this year fifty people took an experimental course at Xerox PARC on knowledge programming in Loops. During the course, they extended and debugged small knowledge systems in a simulated economics domain called Truckin. Everyone learned how to use the environment Loops, formulated the knowledge for their own program, and represented it in Loops. At the end of the course a knowledge competition was run so that the strategies used in the different systems could be compared. The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days. Although one must exercise caution in extrapolating from small experiments, the results suggest that there is substantial power in integrating multiple programming paradigms.