Research in artificial intelligence at the University of Texas at Austin is diverse. It is spread across many departments(Computer Science, Mathematics, the Institute for Computer Science and Computer Applications, and the Linguistics Research Center) and it covers most of the major subareas with AI (natural language, theorem proving, knowledge representation, languages for AI, and applications). Related work is also being done in several other departments, including EE (low-level vision), Psychology, Linguistics, and the Center for Cognitive Science.

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.

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.

Review of "The Fifth Generation

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. The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days.

This report covers two main AI areas: natural language processing and expert systems. The discussion of each area includes an assessment of the state-of-the-art, an enumeration of problems areas and opportunities, recommendations for the next 5-10 years, and an assessment of the resources required to carry them out.

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. 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.)