In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

Abstract: A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user’s view and access data according to the system’s view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query.IBM Journal of Research and Development 20:4, pp. 336-357.

Also see book version (1980), IJCAI-77. Also Rep. No. TR-347, AI Laboratory, Massachusetts Institute of Technology.

Fourth International Joint Conference on Artificial Intelligence Advance Papers, pp. 26B-273

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 frame problem arises in attempts to formalise problem--solving processes involving interactions with a complex world. It concerns the difficulty of keeping track of the consequences of the performance of an action in, or more generally of the making of some alteration to, a representation of the world. The paper contains a survey of the problem, showing how it arises in several contexts and relating it to some traditional problems in philosophical logic. In the second part of the paper several suggested partial solutions to the problem are outlined and compared. This comparison necessitates an analysis of what is meant by a representation of a robot's environment.

From: Kyoto University Research Information Repository

Machine Intelligence 7

See also: Robert Kowalski. 1975. A Proof Procedure Using Connection Graphs. J. ACM 22, 4 (October 1975), 572-595.In B. Meltzer and D. Michie (Eds.), Machine intelligence 7. New York: Wiley, 167-194

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.