Parameter Setting in SAT Solver Using Machine Learning Techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00356267" target="_blank" >RIV/68407700:21240/22:00356267 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=hywaJwCwE/Q=&t=1" target="_blank" >https://www.scitepress.org/PublicationsDetail.aspx?ID=hywaJwCwE/Q=&t=1</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Parameter Setting in SAT Solver Using Machine Learning Techniques
Original language description
Boolean satisfiability (SAT) solvers are essential tools for many domains in computer science and engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Over the course of at least two decades of SAT related research, many variable and value selection heuristics were devised. Heuristics can usually be tuned by single or multiple numerical parameters prior to executing the search process over the concrete SAT instance. In this paper we present a machine learning approach that predicts the parameters of heuristic from the underlying structure of the input SAT instance.
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/GA22-31346S" target="_blank" >GA22-31346S: logicMOVE: Logic Reasoning in Motion Planning for Multiple Robotic Agents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 14th International Conference on Agents and Artificial Intelligence
ISBN
978-989-758-547-0
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
586-597
Publisher name
SciTePress
Place of publication
Madeira
Event location
Online
Event date
Feb 3, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000774441800054