Hierarchical invention of theorem proving strategies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00329371" target="_blank" >RIV/68407700:21730/18:00329371 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3233/AIC-180761" target="_blank" >https://doi.org/10.3233/AIC-180761</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/AIC-180761" target="_blank" >10.3233/AIC-180761</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical invention of theorem proving strategies

Popis výsledku v původním jazyce

State-of-the-art automated theorem provers (ATPs) such as E and Vampire use a large number of different strategies to traverse the search space. Inventing targeted proof search strategies for specific problem sets is a difficult task. Several machine learning methods that invent strategies automatically for ATPs have been proposed previously. One of them is the Blind Strategymaker (BliStr) system for inventing strategies of the E prover. In this paper we describe BliStrTune – a hierarchical extension of BliStr. BliStrTune explores much larger space of E strategies than BliStr by interleaving search for high-level parameters with their fine-tuning. We use BliStrTune to invent new strategies based also on new clause weight functions targeted at problems from large ITP libraries. We show that the new strategies significantly improve E’s performance.

Název v anglickém jazyce

Hierarchical invention of theorem proving strategies

Popis výsledku anglicky

State-of-the-art automated theorem provers (ATPs) such as E and Vampire use a large number of different strategies to traverse the search space. Inventing targeted proof search strategies for specific problem sets is a difficult task. Several machine learning methods that invent strategies automatically for ATPs have been proposed previously. One of them is the Blind Strategymaker (BliStr) system for inventing strategies of the E prover. In this paper we describe BliStrTune – a hierarchical extension of BliStr. BliStrTune explores much larger space of E strategies than BliStr by interleaving search for high-level parameters with their fine-tuning. We use BliStrTune to invent new strategies based also on new clause weight functions targeted at problems from large ITP libraries. We show that the new strategies significantly improve E’s performance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Umělá inteligence a uvažování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

AI Communications

ISSN

0921-7126

e-ISSN

1875-8452

Svazek periodika

31

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

237-250

Kód UT WoS článku

000432544800002

EID výsledku v databázi Scopus

2-s2.0-85047207203