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EMYCIN: A Knowledge Engineerโ€™s Tool for Constructing Rule-Based Expert Systems

Classics

This chapter from the Mycin book is a brief overview of van Melle's Ph.D. dissertation (Stanford, Computer Science), and is a shortened and edited version of a paper appearing in Pergamon-lnfotech state of the art report on machine intelligence, pp. 249-263. Maidenhead, Berkshire, U.K.: Infotech Ltd., 1981. Mycin Book (1984)


Natural Language Understanding

AI Magazine

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.


Utterance and Objective: Issues in Natural Language Communication

AI Magazine

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 โ€œutterance and objectiveโ€ suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.


Cooperative responses from a portable natural language data base query system

Classics

This is a short preview of the document. Your library or institution may give you access to the complete full text for this document in ProQuest.


A Computational Logic

Classics

We discuss the problem of incorporating into a heuristic theorem prover a decision procedure for a fragment of the logic. An obvious goal when incorporating such a procedure is to reduce the search space explored by the heuristic component of the system, as would be achieved by eliminating from the system's data base some explicitly stated axioms. For example, if a decision procedure for linear inequalities is added, one would hope to eliminate the explicit consideration of the transitivity axioms. However, the decision procedure must then be used in all the ways the eliminated axioms might have been. The difficulty of achieving this degree of integration is more dependent upon the complexity of the heuristic component than upon that of the decision procedure. The view of the decision procedure as a black box is frequently destroyed by the need pass large amounts of search strategic information back and forth between the two components.


An algorithm for planning collision-free paths among polyhedral objects

Classics

Get Citation Alerts New Citation Alert added! You will be notified whenever a record that you have chosen has been cited. To manage your alert preferences, click on the button below. Aided Design 8, 1 (Jan. An experimental system for computer controlled mechanical assembly.



A theory of approximate reasoning

Classics

In J. E. Hayes, D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9. Chichester, England: Ellis Horwood Ltd., 149-195


Purposive Understanding

Classics

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.