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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2015: Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques pp 508–517Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Sciences, Saitama University, Saitama, 338-8570, Japan

    Hongbiao Gao, Yuichi Goto & Jingde Cheng

Authors
  1. Hongbiao Gao
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  2. Yuichi Goto
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  3. Jingde Cheng
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Corresponding author

Correspondence to Jingde Cheng .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Gao, H., Goto, Y., Cheng, J. (2015). A Set of Metrics for Measuring Interestingness of Theorems in Automated Theorem Finding by Forward Reasoning: A Case Study in NBG Set Theory. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_50

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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