Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving

7 pages•Published: March 11, 2020

Agnieszka Słowik, Chaitanya Mangla, Mateja Jamnik, Sean Holden and Lawrence Paulson

Abstract

Modern theorem provers such as Vampire utilise premise selection algorithms to control the proof search explosion. Premise selection heuristics often employ an array of continuous and discrete parameters. The quality of recommended premises varies depending on the parameter assignment. In this work, we introduce a principled probabilistic framework for optimisation of a premise selection algorithm. We present results using Sumo Inference Engine (SInE) and the Archive of Formal Proofs (AFP) as a case study. Our approach can be used to optimise heuristics on large theories in minimum number of steps.

Keyphrases: automated theorem proving, bayesian optimisation, heuristic configuration, premise selection, sumo inference engine (sine)

In: Laura Kovacs and Andrei Voronkov (editors). Vampire 2018 and Vampire 2019. The 5th and 6th Vampire Workshops, vol 71, pages 45-51.

Links:	https://easychair.org/publications/paper/K7Zd
	https://doi.org/10.29007/q91g

BibTeX entry

@inproceedings{Vampire2019:Bayesian_Optimisation_Heuristic_Configuration,
  author    = {Agnieszka Słowik and Chaitanya Mangla and Mateja Jamnik and Sean Holden and Lawrence Paulson},
  title     = {Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving},
  booktitle = {Vampire 2018 and Vampire 2019. The 5th and 6th Vampire Workshops},
  editor    = {Laura Kovacs and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {71},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/K7Zd},
  doi       = {10.29007/q91g},
  pages     = {45-51},
  year      = {2020}}

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