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FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover

Cezary Kaliszyk and Josef Urban

Proceedings of the 20th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR-20), Lecture Notes in Computer Science (Advanced Research in Computing and Software Science) 9450, pp. 88 – 96, 2015.

Abstract

FEMaLeCoP is a connection tableau theorem prover based on leanCoP which uses efficient implementation of internal learning-based guidance for extension steps. Despite the fact that exhaustive use of such internal guidance can incur a significant slowdown of the raw inferencing process, FEMaLeCoP trained on related proofs can prove many problems that cannot be solved by leanCoP. In particular on the MPTP2078 benchmark, FEMaLeCoP adds 90 (15.7 %) more problems to the 574 problems that are provable by leanCoP. FEMaLeCoP is thus the first AI/ATP system convincingly demonstrating that guiding the internal inference algorithms of theorem provers by knowledge learned from previous proofs can significantly improve the performance of the provers. This paper describes the system, discusses the technology developed, and evaluates the system.

 

  PDF |    doi:10.1007/978-3-662-48899-7_7  |  © Springer-Verlag Berlin Heidelberg 2015

BibTeX 

@inproceedings{CKJU-LPAR15,
author = "Cezary Kaliszyk and Josef Urban",
title = "{FEMaLeCoP}: Fairly Efficient Machine Learning Connection Prover",
booktitle = "Proceedings of the 20th International Conference on Logic
for Programming, Artificial Intelligence, and Reasoning (LPAR 2015)",
editor = "Martin Davis and Ansgar Fehnker and Annabelle McIver and Andrei Voronkov",
series = "Lecture Notes in Computer Science",
volume = 9450,
pages = "88--96",
year = 2015,
doi = "10.1007/978-3-662-48899-7_7"
}
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