If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
ON CLOSED WORLD DATA BASES Raymond Reiter The University of British Columbia Vancouver, British Columbia ABSTRACT Deductive question-answering systems generally evaluate queries under one of two possible assumptions which we in this paper refer to as the open and closed world assumptions. The open world assumption corresponds to the usual first order approach to query evaluation: Given a data base DB and a query Q, the only answers to Q are those which obtain from proofs of Q given DB as hypotheses. Under the closed world assumption, certain answers are admitted as a result of failure to find a proof. More specifically, if no proof of a positive ground literal exists, then the negation of that literal is assumed true. In this paper, we show that closed world evaluation of an arbitrary query may be reduced to open world evaluation of socalled atomic queries.
This paper explores the truism that people think about what they say. It proposes that, to satisfy their own goals, people often plan their speech acts to affect their listeners' beliefs, goals, and emotional states. Such language use can be modelled by viewing speech acts as operators in a planning system, thus allowing both physical and speech acts to be integrated into plans. Methodological issues of how speech acts should be defined in a planbased theory are illustrated by defining operators for requesting and informing. Plans containing those operators are presented and comparisons ore drawn with Searle's formulation.
Lectured in Philosophy at the Hebrew University In Jerusalem and became Associate Professor in 1957. Since 1957 he has also taught in the Department of History and Philosophy of Science. Joint author with Professor A. A. Fraenkel of "Foundations of Set Theory", to be published by the North-Holland Publishing Company in the series "Studies In Logic". Y. BAR-HILLEL SUMMARY "FOUR sources of inefficiencies in the process of literature searching are briefly described. An "Ideal" solution Is outlined as a frame of reference and its shortcomings discussed.
Dr. Lucien Mehl, born 1919 in Paris, studied at the University, Paris where he obtained his degrees in Philosophy and Law, and a Diploma of Advanced Studies in Political Economy and at the National School of Administration. He is now'Maitre des Requetesi to the Council of State and Director of external training at the National School of Administration. He is a member of the International Fiscal Association, the International Cybernetics Association and the French Operational Research Society. He has published a number of articles on administrative science, law, cybernetics and operational research. LUCIEN HEEL INTRODUCTION I. It may seem an ambitious step to try to apply mechanization or automation to the legal sciences.
R. H. Richens was born in Penge, near London, in 1919. He read natural sciences at Cambridge and is now Assistant Director of the Commonwealth Bureau of Plant Breeding and Genetics at Cambridge. He has been a member of the Cambridge Language Research Group since its foundation. His principal research interests have been the taxonomy and history of the elm, the history of Soviet genetics, and machine translation. Everything symbolized by a set of symbols constitutes the domain of symbolization of the set.
For the past ten years we have been working on the problem of getting a computer to understand natural language. We built an early version of a parser that mapped from English into a language-free representation of the meaning of input sentences (Schank and Tesler, 1969). Simultaneously we worked on the meaning representation itself. We developed Conceptual Dependency which represents meaning as a network of concepts independent of the actual words that might be used to express those concepts (Schank, 1969). Over the years the parser and the representation evolved as we began to understand the complexity of the problem with which we were dealing.
How can we get a computer to understand natural language? Our view of the problem has progressed over the years to a point where an answer to that question today would look quite different from one given ten or even five years ago. Originally, researchers felt that the most relevant issue was syntax. Later, most people agreed that semantics was the most relevant field of study (although few would have agreed on what semantics was). Five years ago, or so, our research was concentrated on finding an adequate meaning representation for sentences.
To use language one must be able to make inferences about the information which language conveys. This is apparent in many ways. For one thing, many of the processes which we typically consider "linguistic" require inference making. For example, structural disambiguation: (1) Waiter, I would like spaghetti with meat sauce and wine. You would not expect to be served a bowl of spaghetti floating in meat sauce and wine.