Lemmatization for Stronger Reasoning in Large Theories

In this work we improve ATP performance in large theories
by the reuse of lemmas derived in previous related problems. Given a
large set of related problems to solve, we run automated theorem provers
on them, extract a large number of lemmas from the proofs found and
post-process the lemmas to make them usable in the remaining problems.

Then we filter the lemmas by several tools and extract their proof
dependencies, and use machine learning on such proof dependencies to
add the most promising generated lemmas to the remaining problems.
On such enriched problems we run the automated provers again,

solving more problems. We describe this method and the techniques we used,
and measure the improvement obtained. On the MPTP2078 large-theory
benchmark the method yields 6.6% and 6.2% more problems proved in
two different evaluation modes.