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.

MIT Press.

Optical transport networks based on wavelength division multiplexing (WDM) are considered to be the most appropriate choice for future Internet backbone. On the other hand, future DOE networks are expected to have the ability to dynamically provision on-demand survivable services to suit the needs of various high performance scientific applications and remote collaboration. Since a failure in aWDMnetwork such as a cable cut may result in a tremendous amount of data loss, efficient protection of data transport in WDM networks is therefore essential. As the backbone network is moving towards GMPLS/WDM optical networks, the unique requirement to support DOE's sciencemore » mission results in challenging issues that are not directly addressed by existing networking techniques and methodologies. The objectives of this project were to develop cost effective protection and restoration mechanisms based on dedicated path, shared path, preconfigured cycle (p-cycle), and so on, to deal with single failure, dual failure, and shared risk link group (SRLG) failure, under different traffic and resource requirement models; to devise efficient service provisioning algorithms that deal with application specific network resource requirements for both unicast and multicast; to study various aspects of traffic grooming in WDM ring and mesh networks to derive cost effective solutions while meeting application resource and QoS requirements; to design various diverse routing and multi-constrained routing algorithms, considering different traffic models and failure models, for protection and restoration, as well as for service provisioning; to propose and study new optical burst switched architectures and mechanisms for effectively supporting dynamic services; and to integrate research with graduate and undergraduate education.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume "Artificial Intelligence in Medicine." Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

Artificial Intelligence Research in the People's Republic of China: A Review

Artificial Intelligence Research in the People's Republic of China: A Review Abstract 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. 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.

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.

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.