14 Heuristic Theory Formation: Data Interpretation, and Rule Formation B. G. Buchanan, E. A. Feigenbaum and N. S. Sridharan

AI Classics/files/AI/classics/Machine_Intelligence_7/Mi-7-Ch14-BuchananFeigenbaumSridharan.pdf 

I. INTRODUCTION Describing scientific theory formation as an information-processing problem suggests breaking the problem into subproblems and searching solution spaces for plausible items in the theory. A computer program called meta-DEN D RAL embodies this approach to the theory formation problem within a specific area of science. Scientific theories are judged partly on how well they explain the observed data, how general their rules are, and how well they are able to predict new events. The meta-D END RA L program attempts to use these criteria, and more, as guides to formulating acceptable theories. The problem for the program is to discover conditional rules of the form S-421, where the S's are descriptions of situations and the A's are descriptions of actions. The rule is interpreted simply as'When the situation S occurs, action A occurs'. The theory formation program first generates plausible A's for theory sentences, then for each A it generates plausible S's. At the end it must integrate the candidate rules with each other and with existing theory. In this paper we are concerned only with the first two tasks: data interpretation (generating plausible A's) and rule formation (generating plausible S's for each A). This paper describes the space of actions (A's), the space of situations (S's) and the criteria of plausibility for both. This requires mentioning some details of the chemical task since the generators and the plausibility criteria gain their effectiveness from knowledge of the task. The theory formation task As in the past, we prefer to develop our ideas in the context of a specific task area.

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