A Set of Metrics for Measuring Interestingness of Theorems in Automated Theorem Finding by Forward Reasoning: A Case Study in NBG Set Theory

The problem of automated theorem finding is one of 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. The problem implicitly requires some metrics to be used for measuring interestingness of found theorems. However, no one...

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Bibliographic Details
Published inIntelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques Vol. 9243; pp. 508 - 517
Main Authors Gao, Hongbiao, Goto, Yuichi, Cheng, Jingde
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Automated theorem finding
Forward reasoning
Graphics programming
Image processing
Metric
NBG set theory
Pattern recognition
Strong relevant logic
Online AccessGet full text
ISBN3319238612
9783319238616
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-23862-3_50

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Summary:The problem of automated theorem finding is one of 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. The problem implicitly requires some metrics to be used for measuring interestingness of found theorems. However, no one addresses that requirement until now. This paper proposes the first set of metrics for measuring interestingness of theorems. The paper also presents a case study in NBG set theory, in which we use the proposed metrics to measure the interestingness of the theorems of NBG set theory obtained by using forward reasoning approach and confirms the effectiveness of the metrics.
ISBN:3319238612
9783319238616
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-23862-3_50