A programming language based on sets; motivation and examples.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.

The equivalence problem for program schemes, or for programs, is reduced to the proving of a theorem in second-order logic. This work extends Manna's first-order logic reductions. Some examples of the technique are given together with a suggested method for obtaining proofs in special cases by firstorder methods. INTRODUCTION Several workers in recent years have considered using techniques and ideas of various mathematical theories of computation for proving interesting results about computer programs. This paper is concerned with two of these approaches.

R.M.BURSTALL 373 23 Assertions: programs written without specifying unnecessary order.

INTRODUCTION Attempts to write'intelligent' computer programs have commonly involved the choice for attack of some particular aspect of intelligent behaviour, together with the choice of some relevant task, or range of tasks, which the program must perform. The emphasis is sometimes on the generality of the program's ability, sometimes on the importance of the particular task which it can perform. Well-known examples of such programs are Newell, Shaw, and Simon's General Problem Solver (1959; see also Ernst and Newell, 1967), which is applicable to a wide range of simple problems, Samuel's checker (draughts) playing program (1959, 1967), and the program written by Evans (1964), which solves geometric analogy problems. However, there is another approach to the goal of machine intelligence which stresses the relationship of an organism to its environment and which sets out from the start to understand what is involved in this relationship. Long ago Grey Walter (1953) experimented with mechanical'tortoises' which could range over the floor in a lifelike manner. Toda (1962), in a whimsical and illuminating paper, has discussed the problems facing an automaton in a simple artificial environment. Friedman (1967), a psychologist, has described a computer simulation of instinctive behaviour involving an automaton equipped with sensory and motor systems. Sandewall (1967) has gone deeply into an automaton/environment relationship with a rather more formal approach. This list is far from complete. In particular, robots of various kinds are under construction at a number of research centres, notably at the Stanford Research Institute (Nilsson and Raphael, 1967). The reader may find it helpful to meditate on the situation of, say, a rat in a cage, as seen by the rat.

Networks as models of word storage: G. R. Kiss

Psychological theory attempts to explain how thinking--the subject matter of psychology--is possible by a brain composed of single mechanistic elements--the basic assumption of psychology. The problem of programming digital computers to behave in complex fashions is equivalent to this aspect of the psychological problem. Today automata theorists agree that no fundamental barrier blocks the development of machines which can think, by any reasonable definition of the term. However, the precise techniques for implementing general thinking proceGseE-J have been only partially developed. An example of a high-level, general thinking process is comprehension: the understanding of passages of a natural language.

The ability of the higher animals to accept and interpret information from distant objects confers enormous advantages for creatures (or machines) which respond only to immediate stimulation and have no opportunity to anticipate the future. Distance receptors, especially the eyes, serve as early warning systems by giving information of distance events, making it possible to gauge the probable future. The classical biological notion of stimulusresponse applies to creatures limited to touch information. The development of distance-receptors evidently allowed brains to develop to give strategic behaviour. It is unfortunate that the early, now classical, studies of reflexes involving touch and the internal regulation of the body have been so largely taken over to describe brain function, for these concepts are inadequate for describing the central nervous system. They tell nothing about how brains handle information from the eyes, to allow animals and man to see. They tell us nothing about decision-making: how present experience is related to the stored past to predict the immediate future.

Using the Neomycin rule base, and inputting Neomycin's own actions to the action justification generator, the average size of J(.4,) was ten and the maximum size was approximately one hundred. When an Odysseus-generated rule base for the Neomycin domain was used, these set sizes increased by a factor of four to five. After the set J(Ai) is generated, the action justification ranking subsystem of Odysseus determines the likelihood that J(Ai) contains ji, the action justification of the specialist. This involves, first, ranking ji,„ in order of likelihood of being equal to the unknown An example of ranking rule is: given two elements of a J(.4,), where,4, occurs early in the problem solving session, the

LOGIC CROUP KNOWLEDGE SYSTEMS LABORATORY Department of Computer Science Stanford tIniversity Stanford, California 91305 Decision Precedures Abstract Distributed artificial intelligence is the study of how a group of individual intelligent agents can combine to solve a difficult global problem. This paper discusses in very general terms the problems of achieving this global goal by considering simpler, local subproblems; we drop the usual requirement that the agents working on the subproblems do not interact. We are led to a single assumption. An example of a distributed computation using these ideas is presented. Introduction The thrust of research in distributed artificial intelligence (DAI) is the investigation of the possibility of solving a difficult problem by presenting each of a variety of machines with simpler parts of it. The approach that has been taken has been to consider the problem of dividing tho original problem: what:,libtasks should be pursued at any given time? To which available machine should a..iven subtask be assigned? The question of how the individual machines should g9 about solving their subproblems has been left to the non-distributed Al community (or perhaps to a recursive application of DAI techniques). The assumption underlying this approach--that each of the agents involved in the solution of the subproblems can proceed independently of the others--has recently been called into question 12,3,6,7,101. It has been realized that, in a world of limited resources, it is inappropriate to dedicate a substantial fraction of those resources to each processor. The increasing attract:witless of parallel architectures in which processors share memory is an example of this: memory is a scarce resource. Automated factories must inevitably encounter similar difficulties. Are the robots working in such factories to be given distinct bins of component parts, and non-overlapping regions in which to work or to travel from one area of the factory to another?