Enhancing ENIGMA Given Clause Guidance

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00329365" target="_blank" >RIV/68407700:21730/18:00329365 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-96812-4_11" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-96812-4_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-96812-4_11" target="_blank" >10.1007/978-3-319-96812-4_11</a>

Result language
angličtina
Original language name
Enhancing ENIGMA Given Clause Guidance
Original language description
ENIGMA is an efficient implementation of learning-based guidance for given clause selection in saturation-based automated theorem provers. In this work, we describe several additions to this method. This includes better clause features, adding conjecture features as the proof state characterization, better data pre-processing, and repeated model learning. The enhanced ENIGMA is evaluated on the MPTP2078 dataset, showing significant improvements.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Artificial Intelligence and Reasoning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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