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.)

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 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.

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.

The following was found in a recycling bin somewhere near Columbia University. You've just admitted that it's grossly flawed; it arrives at years (a hundred years, no need to frame it precisely) to And then disease, diet,, phases of the moondear me, to think. IIumans can learn eventually to do some things well Not t.heir flaws and drawbacks, human beings really can be MARVIN I think you're both confusing the appearance of thinking with the real thing Who's convinced You have t,o AJI then they still don't. EDWARD I give up 1 know a lost cause wtien 1 see one make any decisions that would matter? Human beings cannot be said to i,hink.

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

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.

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. A discussion of possible university-industry-government cooperative efforts is also included.