Automated Invention of Strategies and Term Orderings for Vampire

13 pages•Published: October 19, 2017

Jan Jakubuv, Martin Suda and Josef Urban

Abstract

In this work we significantly increase the performance of the Vampire and E automated theorem provers (ATPs) on a set of loop theory problems. This is done by developing EmpireTune, an AI system that automatically invents targeted search strategies for Vampire and E. EmpireTune extends previous strategy invention systems in several ways. We have developed support for the Vampire prover, further extended Vampire by new mechanisms for specifying term orderings, and EmpireTune can now automatically invent suitable term ordering for classes of problems. We describe the motivation behind these additions, their implementation in Vampire and EmpireTune, and evaluate the systems with very good results on the AIM (loop theory) ATP benchmark.

Keyphrases: automated theorem proving, first order logic, machine learning, parameters learning, proving strategy, strategy invention

In: Christoph Benzmüller, Christine Lisetti and Martin Theobald (editors). GCAI 2017. 3rd Global Conference on Artificial Intelligence, vol 50, pages 121-133.

Links:	https://easychair.org/publications/paper/rsMF
	https://doi.org/10.29007/xghj

BibTeX entry

@inproceedings{GCAI2017:Automated_Invention_Strategies_Term,
  author    = {Jan Jakubuv and Martin Suda and Josef Urban},
  title     = {Automated Invention of Strategies and Term Orderings for Vampire},
  booktitle = {GCAI 2017. 3rd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/rsMF},
  doi       = {10.29007/xghj},
  pages     = {121-133},
  year      = {2017}}

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