Dataset-Specific Strategies for the E Theorem Prover

The E automated theorem proving system has an “automatic” mode that analyzes the input problem in order to choose an effective proof search strategy. A strategy includes the term/literal orderings, given clause selection heuristics, and a number of other parameters. This paper investigates the idea of creating one strategy for a given dataset of problems by merging the strategies chosen by E’s automatic mode over all of the problems in the dataset. This strategy merging approach is evaluated on the MPTPTP2078, VBT, and SLH-29 datasets. Surprisingly, the merged strategies outperform E’s automatic mode over all three datasets.

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Publication Details

Journal
Kalpa publications in computing
Published
2025-04-07
DOI
https://doi.org/10.29007/jn6j
Primary Topic
Neural Networks and Applications
Type
article
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article

Dataset-Specific Strategies for the E Theorem Prover

Jack McKeown, Geoff Sutcliffe
Kalpa publications in computing
Neural Networks and Applications
article

Dataset-Specific Strategies for the E Theorem Prover

Jack McKeown, Geoff Sutcliffe
article en

Abstract

The E automated theorem proving system has an “automatic” mode that analyzes the input problem in order to choose an effective proof search strategy. A strategy includes the term/literal orderings, given clause selection heuristics, and a number of other parameters. This paper investigates the idea of creating one strategy for a given dataset of problems by merging the strategies chosen by E’s automatic mode over all of the problems in the dataset. This strategy merging approach is evaluated on the MPTPTP2078, VBT, and SLH-29 datasets. Surprisingly, the merged strategies outperform E’s automatic mode over all three datasets.

Kalpa publications in computingVol. 21
University of Miami (US)
Openalex Percentile: Top 97%
Neural Networks and Applications
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