MaLeCoP: Machine Learning Connection Prover

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10100552" target="_blank" >RIV/00216208:11320/11:10100552 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/11:00181863

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-22119-4" target="_blank" >http://dx.doi.org/10.1007/978-3-642-22119-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-22119-4" target="_blank" >10.1007/978-3-642-22119-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MaLeCoP: Machine Learning Connection Prover

Popis výsledku v původním jazyce

Probabilistic guidance based on learned knowledge is added to the connection tableau calculus and implemented on top of the leanCoP theorem prover, linking it to an external advisor system. In the typical mathematical setting of solving many problems ina large complex theory, learning from successful solutions is then used for guiding theorem proving attempts in the spirit of the MaLARea system. While in MaLARea learning-based axiom selection is done outside unmodified theorem provers, in MaLeCoP the learning-based selection is done inside the prover, and the interaction between learning of knowledge and its application can be much finer. This brings interesting possibilities for further construction and training of self-learning AI mathematical experts on large mathematical libraries, some of which are discussed. The initial implementation is evaluated on the MPTP Challenge large theory benchmark.

Název v anglickém jazyce

MaLeCoP: Machine Learning Connection Prover

Popis výsledku anglicky

Probabilistic guidance based on learned knowledge is added to the connection tableau calculus and implemented on top of the leanCoP theorem prover, linking it to an external advisor system. In the typical mathematical setting of solving many problems ina large complex theory, learning from successful solutions is then used for guiding theorem proving attempts in the spirit of the MaLARea system. While in MaLARea learning-based axiom selection is done outside unmodified theorem provers, in MaLeCoP the learning-based selection is done inside the prover, and the interaction between learning of knowledge and its application can be much finer. This brings interesting possibilities for further construction and training of self-learning AI mathematical experts on large mathematical libraries, some of which are discussed. The initial implementation is evaluated on the MPTP Challenge large theory benchmark.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Automated Reasoning with Analytic Tableaux and Related Methods

ISBN

978-3-642-22118-7

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

15

Strana od-do

263-277

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Bern

Datum konání akce

4. 7. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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