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.

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.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.

Two closely related aspects of artificial intelligence that have received comparatively little attention in the recent literature are research methodology, and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant as Artificial Intelligence matures into a science and spins off major application efforts. Similarly, awareness of research methodology issues can help plan future research buy learning from past successes and failures. We view the study of research methodology to be similar to the analysis of operational AI techniques, but at a meta-level; that is, research methodology analyzes the techniques and methods used by the researchers themselves, rather than their programs, to resolve issues of selecting interesting and tractable problems to investigate, and of deciding how to proceed with their investigations.

In this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search. Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them.

PROLOG is a simple and powerful progamming language based on first-order logic. This paper examines two possible extensions to the language which would generally be considered "higher-order".t The first extension introduces lambda expressions and predicate variables so that functions and relations can be treated as 'first class' data objects. We argue that this extension does not add anything to the real power of the language. The other extension concerns the introduction of set expressions to denote the set of all (provable) solutions to some goal. We argue that this extension does indeed fill a real gap in the language, but must be defined with care.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

This article is the second planned excerpt from the Handbook of Artificial Intelligence being complied at Stanford University. This overview of the Handbook chapter on search, like the overview of natural language research we printed in the first issue, introduces the important ideas and techniques, which are discussed in detail later in the chapter. Cross-references to other articles in the Handbook have been removed -- terms discussed in more detail elsewhere are italicized. The author would like to note that this article draws on material generously made available by Nils Nilsson for use in the Handbook.

This overview takes a general look at search in problem solving, indicating some connections with topics considered in other Handbook chapters. The these general ideas are found in programs for natural second section considers algorithms that use these language understanding, information retrieval, automatic representations. In methods, which use information about the nature and this chapter of the Handbook we examine search as a tool structure of the problem domain to limit the search. Most of the Finally, the chapter reviews several well-known early examples considered are problems that are relatively easy programs based on search, together with some related to formalize. The first of these is a may be, however, that the description of a task-domain database, which describes both the current task-domain situation is too large for multiple versions to be stored situation and the goal.