Abstract

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.

BibTeX

@inproceedings{CKJUJV-FROCOS2015,
  author    = "Cezary Kaliszyk and Josef Urban and Ji\v{r}\'i Vysko\v{c}il",
  title     = "Lemmatization for Stronger Reasoning in Large Theories",
  booktitle = "Proceedings of the 10th International Symposium on Frontiers
               of Combining Systems (FroCoS 2015)",
  editor    = "Carsten Lutz and Silvio Ranise",
  series    = "Lecture Notes in Artificial Intelligence",
  volume    = 9322,
  pages     = "341--356",
  year      = 2015,
  doi       = "10.1007/978-3-319-24246-0_21"
}