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Learning and discovery: one system's search for mathematical knowledge

 

作者: Susan L. Epstein,  

 

期刊: Computational Intelligence  (WILEY Available online 1988)
卷期: Volume 4, issue 1  

页码: 42-53

 

ISSN:0824-7935

 

年代: 1988

 

DOI:10.1111/j.1467-8640.1988.tb00089.x

 

出版商: Blackwell Publishing Ltd

 

关键词: discovery;machine learning;knowledge representation;concept definition;mathematics;graph theory;découverte;apprentissage automatique;représentation des connaissances;définition de concept;mathématiques;théorie des graphes

 

数据来源: WILEY

 

摘要:

The Graph Theorist, GT, is a system that performs mathematical research in graph theory. From the definitions in its input knowledge base, GT constructs examples of mathematical concepts, conjectures and proves mathematical theorems about concepts, and discovers new concepts. Discovery is driven both by examples and by definitional form. The discovery processes construct a semantic net that links all of GT's concepts together.Each definition is an algebraic expression whose semantic interpretation is a stylized algorithm to generate a class of graphs correctly and completely. From a knowledge base of these concept definitions, GT is able to conjecture and prove such theorems as “The set of acyclic, connected graphs is precisely the set of trees” and “There is no odd‐regular graph on an odd number of vertices.” GT explores new concepts either to develop an area of knowledge or to link a newly acquired concept into a pre‐existing knowledge base. New concepts arise from the specialization of an existing concept, the generalization of an existing concept, and the merger of two or more existing concepts. From an initial knowledge base containing only the definition of “graph,” GT discovers such concepts as acyclic graphs, connected graphs, and b

 

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