Lemmatization for Stronger Reasoning in Large Theories

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F15%3A00235417" target="_blank" >RIV/68407700:21730/15:00235417 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-24246-0_21" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-24246-0_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-24246-0_21" target="_blank" >10.1007/978-3-319-24246-0_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Lemmatization for Stronger Reasoning in Large Theories

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Lemmatization for Stronger Reasoning in Large Theories

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Frontiers of Combining Systems

ISBN

978-3-319-24245-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

16

Strana od-do

341-356

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Wroclaw

Datum konání akce

20. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000366183700021