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Forming Ensembles at Runtime: A Machine Learning Approach

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Forming Ensembles at Runtime: A Machine Learning Approach

  • Original language description

    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.

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

  • Continuities

    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

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

    Leveraging Applications of Formal Methods, Verification and Validation

  • ISBN

    978-3-030-61469-0

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    16

  • Pages from-to

    440-456

  • Publisher name

    Springer

  • Place of publication

    Switzerland

  • Event location

    Rhodes, Greece

  • Event date

    Oct 20, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article