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Detecting Inconsistencies in Large First-Order Knowledge Bases

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Automated Deduction – CADE 26 (CADE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10395))

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Abstract

Large formalizations carry the risk of inconsistency, and hence may lead to instances of spurious reasoning. This paper describes a new approach and tool that automatically probes large first-order axiomatizations for inconsistency, by selecting subsets of the axioms centered on certain function and predicate symbols, and handling the subsets to a first-order theorem prover to test for unsatisfiability. The tool has been applied to several large axiomatizations, inconsistencies have been found, inconsistent cores extracted, and semi-automatic analysis of the inconsistent cores has helped to pinpoint the axioms that appear to be the underlying cause of inconsistency.

J. Urban—Supported by the ERC Consolidator grant no. 649043 AI4REASON.

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Notes

  1. 1.

    http://grid01.ciirc.cvut.cz/~mptp/7.13.01_4.181.1147/.

  2. 2.

    http://grid01.ciirc.cvut.cz/~mptp/7.13.01_4.181.1147/MPTP2/statements.

  3. 3.

    http://grid01.ciirc.cvut.cz/~mptp/7.13.01_4.181.1147/html/.

  4. 4.

    http://grid01.ciirc.cvut.cz/~mptp/7.13.01_4.181.1147/MPTP2/symbols.

  5. 5.

    http://grid01.ciirc.cvut.cz/~mptp/7.13.01_4.181.1147/html/ami_3.html#CC1.

  6. 6.

    https://github.com/JUrban/MPTP2/blob/master/Ebot/loop.pl.

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Author information

Authors and Affiliations

  1. DHBW Stuttgart, Stuttgart, Germany

    Stephan Schulz

  2. University of Miami, Coral Gables, USA

    Geoff Sutcliffe

  3. Czech Technical University in Prague, Prague, Czech Republic

    Josef Urban

  4. Articulate Software, San Francisco, USA

    Adam Pease

Authors
  1. Stephan Schulz
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  2. Geoff Sutcliffe
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  3. Josef Urban
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  4. Adam Pease
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Corresponding authors

Correspondence to Stephan Schulz , Geoff Sutcliffe , Josef Urban or Adam Pease .

Editor information

Editors and Affiliations

  1. Microsoft Research, Redmond, Washington, USA

    Leonardo de Moura

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Schulz, S., Sutcliffe, G., Urban, J., Pease, A. (2017). Detecting Inconsistencies in Large First-Order Knowledge Bases. In: de Moura, L. (eds) Automated Deduction – CADE 26. CADE 2017. Lecture Notes in Computer Science(), vol 10395. Springer, Cham. https://doi.org/10.1007/978-3-319-63046-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-63046-5_19

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