This paper is in the nature of a challenge to artificial intelligence experts. It suggests that the techniques of artificial intelligence should be applied to some realistic problems which exist in the programming and data processing fields. After a brief review of the little related existing work which has been done, the characteristics of programming problems which make them suitable for the application of artificial intelligence techniques are given. Specific illustrations of problems are provided under the broadcategories of data structure and organization, program structure and organization, improvements and corrections of programs, and language.In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London.

"The Meta-DENDRAL program is a vehicle for studying problems of theory formation in science. The general strategy of Meta-DENDRAL is to reason from data to plausible generalizations and then to organize the generalizations into a unified theory. Three main subprobleras are discussed: (1) explain the experimental data for each individual chemical structure, (2) generalize the results from each structure to a l l structures, and (3) organize the generalizations into a unified theory. The program is built upon the concepts and programmed routines already available in the Heuristic DENDRAL performance program, but goes beyond the performance program in attempting to formulate the theory which the performance program will use."In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London. pp. 40-50

This paper echoes, from a philosophical standpoint, the claim of McCarthy and Hayes that Philosophy and Artificial Intelligence have important relations. Philosophical problems about the use of “intuition” in reasoning are related, via a concept of anlogical representation, to problems in the simulation of perception, problem-solving and the generation of useful sets of possibilities in considering how to act. The requirements for intelligent decision-making proposed by McCarthy and Hayes are criticised as too narrow, and more general requirements are suggested instead.See also: Artificial Intelligence, Volume 2, Issues 3–4, Winter 1971, Pages 209–225In IJCAI 1971: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE.. Revised paper in Artificial Intelligence 2:209- 225

This paper describes the programming of a computer controlled arm. The programming is divided logically into planning and execution Communication between planning and execution is by a data fil e which specifies the arm trajectory with reapect to time, and actions that the arm should perform. The servo program which moves the arm along the trajectory is based on Legrangian mechanics and takes into account coupling between links, and the variation of inertial loading with change of arm configuration.In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London.

Proc. Second Intern. Joint Conf. on Artificial Intelligence, 80-7. London: The British Computer Society

Ph.D. thesis, Edinburgh University. This thesis is concerned with algorithms for generating generalisations-from experience. These algorithms are viewed as examples of the general concept of a hypothesis discovery system which, in its turn, is placed in a framework in which it is seen as one component in a multi-stage process which includes stages of hypothesis criticism or justification, data gathering and analysis and prediction. Formal and informal criteria, which should be satisfied by the discovered hypotheses are given. In particular, they should explain experience and be simple. The formal work uses the first-order predicate calculus. These criteria are applied to the case of hypotheses which are generalisations from experience. A formal definition of generalisation from experience, relative to a body of knowledge is developed and several syntactical simplicity measures are defined. This work uses many concepts taken from resolution theory (Robinson, 1965). We develop a set of formal criteria that must be satisfied by any hypothesis generated by an algorithm for producing generalisation from experience. The mathematics of generalisation is developed. In particular, in the case when there is no body of knowledge, it is shown that there is always a least general generalisation of any two clauses, in the generalisation ordering. (In resolution theory, a clause is an abbreviation for a disjunction of literals.) This least general generalisation is effectively obtainable. Some lattices induced by the generalisation ordering, in the case where there is no body of knowledge, are investigated. The formal set of criteria is investigated. It is shown that for a certain simplicity measure, and under the assumption that there is no body of knowledge, there always exist hypotheses which satisfy them. Generally, however, there is no algorithm which, given the sentences describing experience, will produce as output a hypothesis satisfying the formal criteria. These results persist for a wide range of other simplicity measures. However several useful cases for which algorithms are available are described, as are some general properties of the set of hypotheses which satisfy the criteria. Some connections with philosophy are discussed. It is shown that, with sufficiently large experience, in some cases, any hypothesis which satisfies the formal criteria is acceptable in the sense of Hintikka and Hilpinen (1966). The role of simplicity is further discussed. Some practical difficulties which arise because of Goodman's (1965) "grue" paradox of confirmation theory are presented. A variant of the formal criteria suggested by the work of Meltzer (1970) is discussed. This allows an effective method to be developed when this was not possible before. However, the possibility is countenanced that inconsistent hypotheses might be proposed by the discovery algorithm. The positive results on the existence of hypotheses satisfying the formal criteria are extended to include some simple types of knowledge. It is shown that they cannot be extended much further without changing the underlying simplicity ordering. A program which implements one of the decidable cases is described. It is used to find definitions in the game of noughts and crosses and in family relationships. An abstract study is made of the progression of hypothesis discovery methods through time. Some possible and some impossible behaviours of such methods are demonstrated. This work is an extension of that of Gold (1967) and Feldman (1970). The results are applied to the case of machines that discover generalisations. They are found to be markedly sensitive to the underlying simplicity ordering employed.

Communications ACM 8:548-560

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A general game-playing program must know the rules of the particular playing game. These rules are:(1) an algorithm indicating the winning state;(2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation.There are two means of giving these rules:(1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or 'you do not win', or the list of legal moves. But it cannot know what is in that subroutine.(2) We can also define a language in which we describe the rules of a game. The program investigates the rules written with this language and finds some indications to improve its play. Artificial Intelligence and Heuristic Programming Edinburgh University Press

Machine Intelligence 6