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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

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

  • 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

    Proceedings of the 14th International Conference on Agents and Artificial Intelligence

  • ISBN

    978-989-758-547-0

  • ISSN

  • e-ISSN

  • 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