And-or graphs and theorem-proving graphs determine the same kind of search space and differ only in the direction of search: from axioms to goals, in the case of theorem-proving graphs, and in the opposite direction, from goals to axioms, in the case of and-or graphs. We investigate the construction of a single general algorithm which covers unidirectional search both for and-or graphs and for theorem-proving graphs, bidirectional search for path-finding problems and search for a simplest solution as well as search for any solution. Indeed, many different search spaces can represent the same original problem, as in the case of resolution systems where different refinements of the resolution rule determine different search spaces for a single problem of demonstrating the unsatisfiability of a given set of clauses. In the tree representation of theorem-proving graphs (used in Machine Intelligence 5 (Kowalski 1969)), identical problems Tare generated at different times, working forwards from the initial set of axioms {D, E, F, G}.

We were led to this comparison by the observation that the computer model is weaker in three important ways: search depth is not unbounded, structures matching variables cannot be compared, and structures matching variables cannot be moved. Thus, every recursively enumerable language is generated by a transformational grammar with limited search depth, without equality comparisons of variables, and without moving structures corresponding to variables. On the other hand, both mathematical models allow unbounded depth of analysis; both allow equality comparisons of variables, although the Ginsburg-Partee model.compares

This paper describes a computer system for understanding English. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language--syntax, semantics, and inference. It enters into a dialog with a person, responding to English sentences with actions and English replies, asking for clarification when its heuristic programs cannot understand a sentence through the use of syntactic, semantic, contextual, and physical knowledge. By developing special procedural representations for syntax, semantics, and inference, we gain flexibility and power.

Language was considered just a "bunch of words" and the primary task for early machine translation (MT) was to build machines large enough to hold all the words necessary in the translation process. These means included the printing out of the several possible solutions of ambiguous text segments to let the reader decide for himself the correct meaning, printing out the ambiguous source language text, and other temporary expedients. Particularly one must understand the rules under which such a complex system as human language operates and how the mechanism of this operation can be simulated by automatic means, i.e., without any human intervention at all. The second problem, the simulation of human language behavior by automatic means, is almost impossible to achieve, since language is an open and dynamic system in constant change and because the operation of the system is not yet completely understood.

Isard, S. | Longuet-Higgins, H.C.

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 the meantime, Chomsky (1965) devised a paradigm for linguistic analysis that includes syntactic, semantic, and phonological components to account for the generation of natural language statements. This theory can be interpreted to imply that the meaning of a sentence can be represented as a semantically interpreted deep structure--i.e, From computer science's preoccupation with formal programming languages and compilers, there emerged another paradigm. The adoption and combination of these two new paradigms have resulted in a vigorous new generation of language processing systems characterized by sophisticated linguistic and logical processing of well-defined formal data structures. These included a social-conversation machine, systems that translated from English into limited logical calculi, and programs that attempted to answer questions from English text.

We may regard the subject of artificial intelligence as beginning with Turing's article'Computing Machinery and Intelligence' (Turing 1950) and with Shannon's (1950) discussion of how a machine might be programmed to play chess. In this case we have to say that a machine is intelligent if it solves certain classes of problems requiring intelligence in humans, or survives in an intellectually demanding environment. However, we regard the construction of intelligent machines as fact manipulators as being the best bet both for constructing artificial intelligence and understanding natural intelligence. Given this notion of intelligence the following kinds of problems arise in constructing the epistemological part of an artificial intelligence: I.

The generalized resolution principle is a single inference principle which provides, by itself, a complete formulation of the quantifier-free first-order predicate calculus with equality. The completeness theory of the generalized resolution principle exploits the very intuitive and natural idea of attempting to construct counterexamples to the theorems for which proofs are wanted, and makes this the central concept. The expressions are all built up from primitive We shall usually employ lower case letters for individual symbols and upper case letters for function and relation symbols. Similarly, a counterexample to a general proposition is an interpretation which strongly satisfies its premisses but falsifies its conclusion.

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.