Partial Regularization of First-Order Resolution Proofs

12 pages•Published: April 27, 2020

Jan Gorzny, Ezequiel Postan and Bruno Woltzenlogel Paleo

Abstract

Proofs are a key feature of modern propositional and first-order theorem provers. Proofs generated by such tools serve as explanations for unsatisfiability of statements. However, these explanations are complicated by proofs which are not necessarily as concise as possible. There are a wide variety of compression techniques for propositional resolution proofs, but fewer compression techniques for first-order resolution proofs generated by automated theorem provers. This paper describes an approach to compressing first-order logic proofs based on lifting proof compression ideas used in propositional logic to first-order logic. An empirical evaluation of the approach is included.

Keyphrases: first order logic, proof compression, resolution, unification

In: Gregoire Danoy, Jun Pang and Geoff Sutcliffe (editors). GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020), vol 72, pages 34-45.

Links:	https://easychair.org/publications/paper/n62J
	https://doi.org/10.29007/3r41

BibTeX entry

@inproceedings{GCAI2020:Partial_Regularization_First_Order,
  author    = {Jan Gorzny and Ezequiel Postan and Bruno Woltzenlogel Paleo},
  title     = {Partial Regularization of First-Order Resolution Proofs},
  booktitle = {GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020)},
  editor    = {Gregoire Danoy and Jun Pang and Geoff Sutcliffe},
  series    = {EPiC Series in Computing},
  volume    = {72},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/n62J},
  doi       = {10.29007/3r41},
  pages     = {34-45},
  year      = {2020}}

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