Understanding Social Feedback in Biological Collectives with Smoothed Model Checking

Lade...
Vorschaubild
Dateien
Klein_2-tyja396c2vfl9.pdf
Klein_2-tyja396c2vfl9.pdfGröße: 1.34 MBDownloads: 81
Datum
2022
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Bookpart
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
MARGARIA, Tiziana, ed., Bernhard STEFFEN, ed.. Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III. Cham: Springer, 2022, pp. 181-198. Lecture Notes in Computer Science. 13703. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-19758-1. Available under: doi: 10.1007/978-3-031-19759-8_12
Zusammenfassung

Biological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback - how the collective behaviour adapts to changes in the group size - requires dealing with complex models and limited experimental data. In this work, we assume that the collective response is experimentally observed for a chosen, finite set of group sizes. Based on such data, we propose a framework which allows to: (i) predict the collective response for any given group size, and (ii) automatically propose a fitness function. We use Smoothed Model Checking, an approach based on Gaussian Process Classification, to develop a methodology that is scalable, flexible, and data-efficient; We specify the fitness function as a template temporal logic formula with unknown parameters, and we automatically infer the missing quantities from data. We evaluate the framework over a case study of a collective stinging defence mechanism in honeybee colonies.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Social feedback, Gaussian processes, Biological collectives, Smoothed model checking
Konferenz
ISoLA 2022 : Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 22. Okt. 2022 - 30. Okt. 2022, Rhodes, Greece
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690KLEIN, Julia, Tatjana PETROV, 2022. Understanding Social Feedback in Biological Collectives with Smoothed Model Checking. ISoLA 2022 : Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Rhodes, Greece, 22. Okt. 2022 - 30. Okt. 2022. In: MARGARIA, Tiziana, ed., Bernhard STEFFEN, ed.. Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III. Cham: Springer, 2022, pp. 181-198. Lecture Notes in Computer Science. 13703. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-19758-1. Available under: doi: 10.1007/978-3-031-19759-8_12
BibTex
@inproceedings{Klein2022-10-17Under-59812,
  year={2022},
  doi={10.1007/978-3-031-19759-8_12},
  title={Understanding Social Feedback in Biological Collectives with Smoothed Model Checking},
  number={13703},
  isbn={978-3-031-19758-1},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning : 11th International Symposium, ISoLA 2022, Proceedings, Part III},
  pages={181--198},
  editor={Margaria, Tiziana and Steffen, Bernhard},
  author={Klein, Julia and Petrov, Tatjana}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/59812">
    <dc:creator>Petrov, Tatjana</dc:creator>
    <dc:contributor>Klein, Julia</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:language>eng</dc:language>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59812/1/Klein_2-tyja396c2vfl9.pdf"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/59812"/>
    <dc:contributor>Petrov, Tatjana</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59812/1/Klein_2-tyja396c2vfl9.pdf"/>
    <dc:creator>Klein, Julia</dc:creator>
    <dcterms:abstract xml:lang="eng">Biological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback - how the collective behaviour adapts to changes in the group size - requires dealing with complex models and limited experimental data. In this work, we assume that the collective response is experimentally observed for a chosen, finite set of group sizes. Based on such data, we propose a framework which allows to: (i) predict the collective response for any given group size, and (ii) automatically propose a fitness function. We use Smoothed Model Checking, an approach based on Gaussian Process Classification, to develop a methodology that is scalable, flexible, and data-efficient; We specify the fitness function as a template temporal logic formula with unknown parameters, and we automatically infer the missing quantities from data. We evaluate the framework over a case study of a collective stinging defence mechanism in honeybee colonies.</dcterms:abstract>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:title>Understanding Social Feedback in Biological Collectives with Smoothed Model Checking</dcterms:title>
    <dcterms:issued>2022-10-17</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-19T09:11:11Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-19T09:11:11Z</dc:date>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen