Bayesian Ranking for Strategy Scheduling in Automated Theorem Provers
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Mangla, C https://orcid.org/0000-0001-7600-1681
Holden, SB https://orcid.org/0000-0001-7979-1148
Paulson, LC https://orcid.org/0000-0003-0288-4279
Abstract
jats:titleAbstract</jats:title>jats:pAjats:italicstrategy schedule</jats:italic>allocates time to proof strategies that are used in sequence in a theorem prover. We employ Bayesian statistics to propose alternative sequences for the strategy schedule in each proof attempt. Tested on the TPTP problem library, our method yields a time saving of more than 50%. By extending this method to optimize the fixed time allocations to each strategy, we obtain a notable increase in the number of theorems proved.</jats:p>
Description
Keywords
Bayesian machine learning, Strategy scheduling, Automated theorem proving
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name
International Joint Conference on Automated Reasoning 2022
Journal ISSN
0302-9743
1611-3349
1611-3349
Volume Title
Publisher
Springer International Publishing
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (1788755)
European Research Council (742178)
Engineering and Physical Sciences Research Council (EP/P020259/1)
European Research Council (742178)
Engineering and Physical Sciences Research Council (EP/P020259/1)
This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through a Doctoral Training studentship, award reference 1788755.