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

Session 6 Logic: II Theorem Proving and STEPS TOWARD AUTOMATIC THEORY FORMATION John Seely Brown Information and Computer Science Department University of California Irvine Irvine, California Abstract This paper describes a theory formation system which can discover a partial axiomization of a data base represented as extensionally defined binary relations.- The system first discovers all possible intensional definitions of each binary relation in terms of the others. It then determines a minimal set of these relations from which the others can be defined. It then attempts to discover all the ways the relations of this minimal set can interact with each other, thus generating a set of inference rules. Although the system was originally designed to explore automatic techniques for theory construction for question-answering systems, it is currently being expanded to function as a symbiotic system to help social scientists explore certain kinds of data bases. Introduction For over a decade researchers in AI have been designing question-answering systems which are capable of deriving "implicit" facts from a sparse data base.

A theorem prover for part of arithmetic in described which proves theorems by representing them in the form of a diagram or network. The nodes of this network represent 'ideal integers', i.e. objects which have all the properties of integers, without being any particular intoger. The links in the network represent relationships between 'ideal integers'. The procedures which draw these diagrams make elementary deductions based on their built-in knowledge of the functions and predicates of arithmetic. This theorem prover is intended as a model of some kinds of human problem-solving behaviour. Also found at EdinburghIn IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

The object is segmented into parts by grouping parallel traces obtained from the ranging system. Making use of the property of generalized translational invariance, the parts are described in terms of generalized cylinders, consisting of a space curve, or axis, and a circular cross section function on this axis.

L. Peter Deutsch Xerox corporation, Palo Alto Research center (PARC) Palo Alto, California 94304 Abstract This paper presents a machine designed for compact representation and rapid execution of LISP programs. The machine language is a factor of 2 to 5 more compact than S-expressions or conventional compiled code, and the.compiler is extremely simple. The encoding scheme is potentially applicable to data as well as program. The machine also provides for user-defined data structures. Introduction Pew existing computers permit convenient or efficient implementation of dynamic storage allocation, recursive procedures, or operations on data whose type is represented explicitly at run time rather than determined at compile time. This mismatch between machine and language design plagues every implementor of languages designed for manipulation of structured information.

This paper describes the structure and operation of the Hearsay speech understanding system by the use of a specific example illustrating the various stages of recognition. The system consists of a set of cooperating independent processes, each representing a source of Knowledge. The knowledge is used either to predict what may appear in a given context or to verify hypotheses resulting from a prediction. The structure of the system is illustrated by considering its Operation in a particular task situation: Voice-Chess. The representation and use of various sources of knowledge are outlined. Preliminary results of the reduction in search resulting from the use of various sources of knowledge are given.See also: IEEE Transactions on Computers C-25:427-431.(1976).In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

At the present stage of research in artificial intelligence , machines are stil l remote from achieving a level of intelligence comparable in complexity to human thought. As computer applications become more sophisticated, however, and thus more influential in human affairs , it becomes increasingly important to understand both the capabilities and limitations of machine Intelligence and its potential impact on society. To this end, the artificial intelligence field was ex­amined in a systematic manner. The study was divided into two parts : (1) Delineation of areas of artificial intelligence, and postulatio " of hypothetical prod­ucts resulting from progress in the field , and (2) A judgmental portion, which involved appli­cations and implications of the products to society . For the latter purpose, a Delphi study was conducted among experts in the artificial intelligence field to solicit their opinion concerning prototype and com­mercial dates for the products, and the possibility and desirability of their applications and implications .In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

This framework is in large part due to various res trictions imposed upon the heuristic that guides the search and the resulting effect on the search algorithm itself. In order to discuss some of these restrictions it is necessary to introduce the following notation. For a node n of a tree or graph, the following functions are defined as part of the problem.

David E. Rumelhart Donald A. Norman Department of Psychology University of California, San Diego La Jolla, California 92037 Abstract A general system to simulate human cognitive processes is described. The four-part system comprises a nodespace to store the network structure; a supervisor; a transition network parser; and an interpreter. The method by which noun phrases operate and the process for the determiner "the" is presented. An analysis of verb structures illustrates how network structures can be constructed from primitive verb definitions that get at the underlying structures of particular verbs. The paper concludes with an illustration of a problem in question-asking. A Model of Human Memory We have constructed a large general simulation of human language and long-term memory on the premise that the study of the interrelationships among psychological processes will lead to more insight into human cognition and memory. The general implementation is basically complete, and a variety of users are starting to study specific psychological tasks (language understanding; children's development of language; primitive verb structure; reading; inference; game playing--Go and Gomoku; visual representation and memory; learning; and question answering). It is still too early to report on the results of the psychological investigation.. Therefore, this paper is a progress report on the system and the underlying psychological principles. The major guidelines have come from our attempts to represent long-term memory structures. We know that people rapidly forget the details about the surface structure of an experience but retain the meaning or interpretation of that experience indefinitely.

This paper presents a framework for characterizing automatic programming systems in terms of how a task is communicated to the system, the method and time at which the system acquires the knowledge to perform the task, and the characteristics of the resulting program to perform that task. It describes one approach In which both tasks and knowledge about the task domain are stated in natural language In the terms of that domain. All knowledge of computer science necessary to implement the task is internalized inside the system.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California, pp.494-499