Contradiction Detection and Repair in a Large Theory

Authors

  • Adam Pease Articulate Software
  • Stephan Schulz DHBW Stuttgart

DOI:

https://doi.org/10.32473/flairs.v35i.130691

Abstract

As with any software, the challenges of developing large and
manually-created axiomatizations in an expressive logic such
as first order logic with equality can be very different from
those found in comparatively small theories. We present some
of the tools and practices that have supported development of
a logical theories with tens of thousands of statements, and
ensured that they are free of logical contradiction, and suit-
able for automated theorem reasoning.

Accessibility Summary:

In accordance with Title II regulations this content meets all points of exemption as Archived web content and/or Preexisting conventional electronic documents.

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Published

04-05-2022

How to Cite

Pease, A., & Schulz, S. (2022). Contradiction Detection and Repair in a Large Theory. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130691