Regularization in Spider-Style Strategy Discovery and Schedule Construction
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Abstract (arXiv)
To achieve the best performance, automatic theorem provers often rely on schedules of diverse proving strategies to be tried out (either sequentially or in parallel) on a given problem. In this paper, we report on a large-scale experiment with discovering strategies for the Vampire prover, targeting the FOF fragment of the TPTP library and constructing a schedule for it, based on the ideas of Andrei Voronkov's system Spider. We examine the process from various angles, discuss the difficulty (or ease) of obtaining a strong Vampire schedule for the CASC competition, and establish how well a schedule can be expected to generalize to unseen problems and what factors influence this property.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 19 Mar 2024.
Builds on
6 of 41 references.
References · 6
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 230
- 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 89
- 2022A Survey of Methods for Automated Algorithm Configurationcited by 30
- 2023MizAR 60 for Mizar 50cited by 6
- 2016BliStrTune: Hierarchical Invention of Theorem Proving Strategiescited by 5
- 2013MaLeS: A Framework for Automatic Tuning of Automated Theorem Proverscited by 3
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.
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- 2025Efficient Neural Clause-Selection Reinforcementcited by 0
- 2025Efficient Neural Clause-Selection Reinforcement10 Mar 2025
- 2024Machine Learning for Quantifier Selection in cvc526 Aug 2024