A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

The Yale Artificial Intelligence Project, under the direction of Professor Roger C. Schank, supports a number of research projects. Most of this research is in the02-02 area of attempting to model the processes involved in human understanding, with a current emphasis on memory models and the processes involved in learning.

Advanced Information and Decision Systems (AI-DS) is a relatively new, employee-owned company that does basic and applied research, product development, and consulting in the fields of artificial intelligence, computer science, decision analysis, operations research, control theory, estimation theory, and signal processing. AI&DS performs studies, analyses, systems design and evaluation, and software development for a variety of industrial clients and government agencies, including the Department of Defense and Energy.

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.

At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to AI in the past. At the same time, many of the established AI centers are in the process of converting from older facilities, primarily based on Decsystem-10 and Decsystem-20 machines, to a variety of newer options. At present, unfortunately, there is no simple answer to the question of what machines, operating systems, and languages a new or upgrading AI facility should use, and this situation has led to a great deal of confusion and anxiety on the part of those researchers and administrators who are faced with making this choice. In this article I will survey the major alternatives available at present and those that are clearly visible on the horizon, and I will try to indicate the advantages and disadvantages of each for AI work. This is mostly information that we have gathered at CMU in the course of planning for our own future computing needs, but the opinions expressed are my own.

A four-volume collection of articles on all the major topics of AI at that time, with an extensive bibliography. Vol I (Avron Barr and Edward A. Feigenbaum, 1981) (https://books.google.com/books?isbn=1483214370). Vol II (Avron Barr, Edward A. Feigenbaum, Paul R. Cohen, 1982) (https://books.google.com/books?isbn=1483214389). Vol III (Paul R. Cohen and Edward A. Feigenbaum, 1982) (https://books.google.com/books?isbn=1483214397). Vol IV (Avron Barr and Paul R. Cohen, 1989) (https://books.google.com/books?isbn=1483214370). Reading, Mass.: Addison-Wesley.

Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this article. First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. Second, as the phrase “utterance and objective” suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.

This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of overview articles like this one, which attempt to give historical and scientific perspective to work in the different areas of AI research. This article is from the Handbook chapter on natural language understanding. Cross-references to other articles in the handbook have been removed-terms discussed in more detail elsewhere are italicized. Many people have contributed to this chapter, including especially Anne Gardner, James Davidson, and Terry Winograd. Avron Barr and Edward A. Feigenbaum are the Handbook's general editors.

The NLC system has grown out of an earlier series of studies on the "autoprogrammer" (Biermann[6]) and bears much resemblance to it. Program synthesis in both the current and the previous systems is based upon example calculations done by the user on displayed data structures. In the current system, the example is done in restricted English with all its power, which is a dramatic departure from the earlier approach, which simply involved pointing with a light pen. However, it is expected that many of the features from the autoprogrammer, such as "continue" and "automatic indexing", will transfer quite naturally into NLC. This paper emphasizes the natural language aspects of the system, while other reports deal with some of the additional automatic programming features. The relationship of NLC to other research in natural language processing is discussed in a later section. The next section presents an overview of NLC, after which subsequent sections discuss scanning, syntactic and semantic processing, and interpretation of commands in the "matrix computer". The next two sections discuss the processing of flow-of-control commands and the level of behavior achieved by the system. The final sections include a discussion of related research and conclusions.