3 research outputs found
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Discovering qualitative empirical laws
In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition
Artificial Intelligence and Human Error Prevention: A Computer Aided Decision Making Approach: Technical Report No. 4: Survey and Analysis of Research on Learning Systems from Artificial Intelligence
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryU.S. Department of Transportation / DOT FA79WA-4360 ABFederal Aviation Administratio
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Steps Toward Automatic Theory Formation
This paper describes a theory formation system which can discover a partial axiomization of a data base represented as extensionally defined binary relations. The system first discovers all possible intensional definitions of each binary relation in terms of the others. It then determines a minimal set of these relations from which the others can be defined. It then attempts to discover all the ways the relations of this minimal set can interact with each other, thus generating a set of inference rules. Although the system was originally designed to explore automatic techniques for theory construction for question-answering systems, it is currently being expanded to function as a symbiotic system to help social scientists explore certain kinds of data bases