Since 1967 there has again been great interest in chess programming. This paper demonstrates that the structure of today's most successful programs cannot be extended to play Master level chess. Certain basic requirements of a Master player's performance are shown to be outside the performance limits to which a program of this type could be extended. The paper also examines a basic weakness in the tree-searching model approach when applied to situations that cannot be searched to completion. This is the Horizon Effect, which causes unpredictable evaluation errors due to an interaction between the static evaluation function and the rules for search termination. The outline of a model of chess playing that avoids the Horizon Effect and appears extendable to play Master level chess is presented, together with some results already achieved In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California, pp. 77-85

This paper considers various factors affecting system organization for speech understanding research. The structure of the Hearsay system based on a set of cooperating, independent processes using the hypothesize-and-test paradigm is presented. Design considerations for the effective use of multiprocessor and network architectures in speech understanding systems are presented: control of processes, interprocess communication and data sharing, resource allocation, and debugging are discussed.See also: IEEE Xplore.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

This work deals with computer analysis of textured surfaces. Descriptions of textures are form­alized from natural language descriptions. Local texture descriptions are obtained from the directional and non-directional components of the Fourier transform power spectrum. Analytic expressions are de­rived for orientation, contrast, size, spacing, and in periodic cases, the locations of texture elements. The local descriptions are defined over windows of varying sizes.See also: ACM Digital Library.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, Stanford University Stanford, California, 20-23 August

IEEE Transactions on Systems, Man, and Cybernetics SMC-3:610-621

The aim of this paper is to present in a rigorous way the syntax and semantics of a certain fragment of a certain dialect of English. Patrick Suppes claims, in a paper prepared for the present workshop [the 1970 Stanford Workshop on Grammar and Semantics], that at the present time the semantics of natural languages are less satisfactorily formulated than the grammars ¼ [and] a complete grammar for any significant fragment of natural language is yet to be written.'' This claim would of course be accurate if restricted in its application to the attempts emanating from the Massachusetts Institute of Technology, but fails to take into account the syntactic and semantic treatments proposed in Montague (1970a, b). Thus the present paper cannot claim to present the first complete syntax (or grammar, in Suppes' terminology) and semantics for a significant fragment of natural language; and it is perhaps not inappropriate to sketch relations between the earlier proposals and the one given below. Montague (1970b) contains a general theory of languages, their interpretations, and the inducing of interpretations by translation.

Dept. of Computer Science, Carnegie-Mellon University. "Many game-playing programs must search very large game trees. Use of the alpha-beta pruning algorithm instead of the simple minimax search reduces by a large factor the number of bottom positions which must be examined in the search. An analytical expression for the expected number of bottom positions examined in a game tree using alpha-beta pruning is derived, subject to the assumptions that the branching factor N and the depth D of the tree are arbitrary but fixed, and the bottom positions are a random permutation of ND unique values. A simple approximation to the growth rate of the expected number of bottom positions examined is suggested, based on a Monte Carlo simulation for large values of N and D. The behavior of the model is compared with the behavior of the alpha-beta algorithm in a chess playing program and the effects of correlation and non-unique bottom position values in real game trees are examined."

In recent years many attempts have been made to use the computer as an aid to diagnosis, but little has been done to exploit the potential of computer technology as a more general aid to decision making. We describe the use of the discipline of decision analysis as the basis for an experimental interactive computer program designed to assist the physician in the clinical management of acute oliguric renal failure. The program deals with alternative courses of action, either tests or treatments, for which the potential risks or benefits may be large, and it balances the anticipated risk of a given strategy against the anticipated benefit that it offers the patient. The appraisals of the different courses of action open to the physician are expressed in quantitative terms as expected value. The program has been evaluated by comparing its recommendations to those of experienced nephrologists in 18 simulated cases of acute oliguric renal failure.

In R.C. Schank and K.M. Colby, Computer Models of Thought and Language, pp. 287-339. Limited Preview (Table of Contents and Index) (https://archive.org/details/computermodelsof00scha).

"A program that achieves the interpretation of line drawings as polyhedral scenes is described. The method is based on general coherence rules that the surfaces and edges must satisfy, thereby avoiding the use of predetermined interpretations of particular categories of picture junctions and corners." The paper also comments on the relationship of this program to four other scene analysis programs.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California. Revised version in Artificial Intelligence 4:121-137.

In Rustin, 111-154.