"Heuristic DENDRAL is a computer program written to solve problems of inductive inference in organic chemistry. This paper will use the design of Heuristic DENDRAL and its performance on different problems for a discussion of the following topics: 1. the design for generality; 2. the performance problems attendant upon too much generality; 3. the coupling of expertise to the general problem solving processes; 4. the symbiotic relationship between generality and expertness, and the implications of this symbiosis for the study and design of problem solving systems. We conclude the paper with a view of the design for a general problem solver that is a variant of the "big switch" theory of generality."See also: Stanford University Report (ACM Citation)In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, pp. 165–190. Edinburgh University Press

Sociologists are concerned to predict the effect of changes on future society.But is prediction in principle possible when intelligence is involved? Ifintelligence is the production of novelty, accurate prediction might seem to bestrictly impossible. However this may be, it seems that the present troubleabout social prediction is simply that there are no adequate theoreticalmodels of societies. This means that politicians are almost powerless topredict, plan, or control, except with incredible errors. We find ourselves injust this position in trying to assess the implications of future intelligence.Machine Intelligence 6

"Problem-solving methods using some sort of heurstically guided search process have been the subject of much research in Artificial Intelligence. This paper groups these problem-solving methods under three major headings: the State-Space Approach, the Problem-Reduction Approach and the Formal-Logic Approach." New York: McGraw-Hill.

The problem we consider in this paper is that of discovering formal ruleswhich will enable us to decide when a question posed in English can beanswered on the basis of one or more declarative English sentences. Toillustrate how this may be done in very simple cases we give rules whichtranslate certain declarative sentences and questions involving the quantifiers'some', 'every', 'any', and 'no' into a modified first-order predicate calculus,and answer the questions by comparing their translated forms with those ofthe declaratives. We suggest that in order to capture the meanings of morecomplex sentences it will be necessary to go beyond the first-order predicatecalculus, to a notation in which the scope of words other than quantifiersand negations is clearly indicated.Machine Intelligence 6

In N. V. Findler and B. Meltzer (Eds.), Artificial intelligence and heuristic programming. New York: American Elsevier, 81-100.

We describe the analysis of visual scenes consisting of black on white drawings formed with curved lines, depicting familiar objects and forms: houses, trees, persons, and so on; for instance, drawings found in coloring books. The goal of such analysis is to recognize (by computer) such forms and shapes when present in the input scene; that is, to name (correctly) as many parts of the scene as possible: finger, hand, girl, dance, and so on. Complications occur because each input scene contains several such objects, partially occluding each other and in varying degrees of orientation, size, and so on. The analysis of these line drawings is an instance of'the context problem', which can be stated as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because each shape allows a component to have one of several possible values (a circle can be sun, ball, eye, hole) or meanings, can we make use of context information stated in the form of models, in order to single out for each component a value in such manner that the whole set (scene) is consistent or makes global sense?' Thus, shape drastically limits the values that a component could have, and further disambiguation is possible only by using global information (derived from several components and their interrelations or interconnections) under the assumption that the scene as a whole is meaningful. This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. We have not implemented this approach; indeed, a purpose of this paper is to collect criticisms and suggestions.

In IJCAI 1971: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE.

In R. J. O'Brien (Ed.), Report of the twenty-second annual round table meeting on linguistics and language studies. (Monograph Series on Languages and Linguistics, No. 24.) Washington, D.C.: Georgetown University Press, 35-56.

Invited paper presented at the IEEE Systems Science and Cybernetics Conference, Miami