Repository logo
 

Bayesian Ranking for Strategy Scheduling in Automated Theorem Provers

Accepted version
Peer-reviewed

Type

Conference Object

Change log

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

Volume Title

Publisher

Springer International Publishing
Sponsorship
Engineering and Physical Sciences Research Council (1788755)
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.