Simple Dataset for Proof Method Recommendation in Isabelle/HOL
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00346115" target="_blank" >RIV/68407700:21230/20:00346115 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-53518-6_21" target="_blank" >https://doi.org/10.1007/978-3-030-53518-6_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-53518-6_21" target="_blank" >10.1007/978-3-030-53518-6_21</a>
Alternative languages
Result language
angličtina
Original language name
Simple Dataset for Proof Method Recommendation in Isabelle/HOL
Original language description
Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers. However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Isabelle/HOL, from achieving a large scale success in this field. In this data description, we present a simple dataset that contains data on over 400k proof method applications along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic. Our simple data format allows machine learning practitioners to try machine learning tools to predict proof methods in Isabelle/HOL without requiring domain expertise in logic
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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/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)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science
ISBN
978-3-030-53517-9
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
6
Pages from-to
297-302
Publisher name
Springer
Place of publication
Cham
Event location
Bertinoro, Forli
Event date
Jul 26, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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