FEMaLeCoP: Fairly Efficient Machine Learning Connection Prover

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.