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Forming Ensembles at Runtime: A Machine Learning Approach

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10415586" target="_blank" >RIV/00216208:11320/20:10415586 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://doi.org/10.1007/978-3-030-61470-6_26" target="_blank" >https://doi.org/10.1007/978-3-030-61470-6_26</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-61470-6_26" target="_blank" >10.1007/978-3-030-61470-6_26</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Forming Ensembles at Runtime: A Machine Learning Approach

  • Popis výsledku v původním jazyce

    mart system applications (SSAs) built on top of cyberphysical and socio-technical systems are increasingly composed of components that can work both autonomously and by cooperating with each other. Cooperating robots, fleets of cars and fleets of drones, emergency coordination systems are examples of SSAs. One approach to enable cooperation of SSAs is to form dynamic cooperation groups-ensembles-between components at runtime. Ensembles can be formed based on predefined rules that determine which components should be part of an ensemble based on their current state and the state of the environment (e.g., &quot;group together 3 robots that are closer to the obstacle, their battery is sufficient and they would not be better used in another ensemble&quot;). This is a computationally hard problem since all components are potential members of all possible ensembles at runtime. In our experience working with ensembles in several case studies the past years, using constraint programming to decide which ensembles should be formed does not scale for more than a limited number of components and ensembles. Also, the strict formulation in terms of hard/soft constraints does not easily permit for runtime self-adaptation via learning. This poses a serious limitation to the use of ensembles in large-scale and partially uncertain SSAs. To tackle this problem, in this paper we propose to recast the ensemble formation problem as a classification problem and use machine learning to efficiently form ensembles at scale.

  • Název v anglickém jazyce

    Forming Ensembles at Runtime: A Machine Learning Approach

  • Popis výsledku anglicky

    mart system applications (SSAs) built on top of cyberphysical and socio-technical systems are increasingly composed of components that can work both autonomously and by cooperating with each other. Cooperating robots, fleets of cars and fleets of drones, emergency coordination systems are examples of SSAs. One approach to enable cooperation of SSAs is to form dynamic cooperation groups-ensembles-between components at runtime. Ensembles can be formed based on predefined rules that determine which components should be part of an ensemble based on their current state and the state of the environment (e.g., &quot;group together 3 robots that are closer to the obstacle, their battery is sufficient and they would not be better used in another ensemble&quot;). This is a computationally hard problem since all components are potential members of all possible ensembles at runtime. In our experience working with ensembles in several case studies the past years, using constraint programming to decide which ensembles should be formed does not scale for more than a limited number of components and ensembles. Also, the strict formulation in terms of hard/soft constraints does not easily permit for runtime self-adaptation via learning. This poses a serious limitation to the use of ensembles in large-scale and partially uncertain SSAs. To tackle this problem, in this paper we propose to recast the ensemble formation problem as a classification problem and use machine learning to efficiently form ensembles at scale.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/8A18006" target="_blank" >8A18006: Aggregate Farming in the Cloud</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    Leveraging Applications of Formal Methods, Verification and Validation

  • ISBN

    978-3-030-61469-0

  • ISSN

    0302-9743

  • e-ISSN

  • Počet stran výsledku

    16

  • Strana od-do

    440-456

  • Název nakladatele

    Springer

  • Místo vydání

    Switzerland

  • Místo konání akce

    Rhodes, Greece

  • Datum konání akce

    20. 10. 2020

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku