Domain Dependent 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%3A00362980" target="_blank" >RIV/68407700:21240/22:00362980 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-22953-4_8" target="_blank" >https://doi.org/10.1007/978-3-031-22953-4_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-22953-4_8" target="_blank" >10.1007/978-3-031-22953-4_8</a>
Alternative languages
Result language
angličtina
Original language name
Domain Dependent Parameter Setting in SAT Solver Using Machine Learning Techniques
Original language description
We address the problem of variable and truth-value choice in modern search-based Boolean satisfiability (SAT) solvers depending on the problem domain. The SAT problem is the task to determine truth-value assignment for variables of a given Boolean formula under which the formula evaluates to true. The SAT problem is often used as a canonical representation of combinatorial problems in many domains of computer science ranging from artificial intelligence to software engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Many efficient variable and truth-value selection heuristics were devised. Heuristics can usually be fine 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 a graph derived from the input SAT instance. Using this approach we effectively fine tune the SAT solver for specific problem domain.
Czech name
—
Czech description
—
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/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
Agents and Artificial Intelligence 14th International Conference, ICAART 2022 Virtual Event, February 3–5, 2022 Revised Selected Papers
ISBN
978-3-031-22952-7
ISSN
2945-9133
e-ISSN
1611-3349
Number of pages
32
Pages from-to
169-200
Publisher name
Springer International Publishing AG
Place of publication
Cham
Event location
Online
Event date
Feb 3, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000971480800008