Improving ENIGMA-style Clause Selection while Learning From History
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00353738" target="_blank" >RIV/68407700:21730/21:00353738 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-79876-5_31" target="_blank" >https://doi.org/10.1007/978-3-030-79876-5_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-79876-5_31" target="_blank" >10.1007/978-3-030-79876-5_31</a>
Alternative languages
Result language
angličtina
Original language name
Improving ENIGMA-style Clause Selection while Learning From History
Original language description
We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recogniz ing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We pro pose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The auto matic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/GJ20-06390Y" target="_blank" >GJ20-06390Y: Powering Automatic Theorem Provers by Machine Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Automated Deduction – CADE 28
ISBN
978-3-030-79875-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
19
Pages from-to
543-561
Publisher name
Springer, Cham
Place of publication
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Event location
Pittsburgh
Event date
Jul 12, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000693448800031