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) …
To illustrate how this may be done in very simple cases we give rules which translate certain declarative sentences and questions involving the quantifiers'some', 'every', 'any', and'no' into a modified first-order predicate calculus, and answer the questions by comparing their translated forms with those of the declaratives. John kissed Mary (1) Did John kiss Mary? (5) We begin by describing a method for translating a modest subset of English into a slightly modified first-order predicate calculus -- modified just enough to provide a representation for questions. We would like to have rules which transcribe such declarative sentences into predicate calculus formulae, such as VxMxj (7') 3x-- The matrix will be preceded by a string of quantifiers and negations -- and possibly a question mark; we have found that the transcription rules which appear below produce unique and acceptable orderings of these symbols from unambiguous sentences of the specified type.
IN THIS PAPER, PREPARED FOR THE APRIL 1967 TEXAS SYMPOSIUM ON LINGUISTIC UNIVERSALS, IT IS PROPOSED THAT THE GRAMMATICAL NOTION "CASE" DESERVES A PLACE IN THE BASE COMPONENT OF THE GRAMMAR OF EVERY LANGUAGE. IT IS ARGUED THAT PAST RESEARCH HAS NOT LED TO VALID INSIGHTS ON CASE RELATIONSHIPS AND THAT WHAT IS NEEDED IS A CONCEPTION OF BASE STRUCTURE IN WHICH CASE RELATIONSHIPS ARE PRIMITIVE TERMS OF THE THEORY AND IN WHICH SUCH CONCEPTS AS "SUBJECT" AND "DIRECT OBJECT" ARE MISSING.
INTRODUCTION In this paper we describe a program which will assign deep and surface structure analyses to an infinite number of English sentences.1 The design of this program differs in several respects from that of other automatic parsers presently in existence. Among the most notable of these features is the program's ability to assign syntactic labels to an infinite number of words while operating with a finite dictionary. But undoubtedly the most important decision that resulted from our attempt to construct a model for the perception of syntactic structure was our decision that the program should assign both deep and surface structure analyses to sentences. There is a good deal of evidence to suggest that the efficiency with which human beings recognize the syntactic structure of sentences is to some extent the result of their ability, having heard part of a sentence, to predict the structure of the remainder.
The two primary components of the experimental computer program consisted of a phrase structure generation grammar capable of generating grammatical nonsense, and a monitoring system which would abort the generation process whenever it was apparent that the dependency structure of a sentence being generated was not in harmony with the dependency relations existing in an input source text. Potential applications include automatic kernelizing, question answering, automatic essay writing, and automatic abstracting systems. Introduction This paper sets forth the hypothesis that there is in the English language a general principle of transitivity of dependence among elements and describes an experiment in the computer generation of coherent discourse that supports the hypothesis. Given as input a set of English sentences, if we hold constant the set of vocabulary tokens and generate grammatical English statements from that vocabulary with the additional restriction that their transitive dependencies agree with those of the input text, the resulting sentences will all be truth-preserving paraphrases derived from the original set.