Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing

This work was supported by the German Niedersächsisches Ministerium für Wissenschaftund Kultur (MWK) in the programme PROFILinternational, as well as the Spanish Ministry of Science and Innovation, co-funded by EU FEDER Funds, through grants RTI2018-095390-B-C33, PID2021-123673OB-C32 and TED2021-131...

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Detalles Bibliográficos
Autores: Bruns, Ralf, Dötterl, Jeremias, Dunkel, Jürgen, Ossowski, Sascha
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/27713
Acceso en línea:https://hdl.handle.net/10115/27713
Access Level:acceso abierto
Palabra clave:crowdsourcing
data stream learning
multiagent systems
collaborative coordination
market-based coordination
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spelling Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile CrowdsourcingBruns, RalfDötterl, JeremiasDunkel, JürgenOssowski, Saschacrowdsourcingdata stream learningmultiagent systemscollaborative coordinationmarket-based coordinationThis work was supported by the German Niedersächsisches Ministerium für Wissenschaftund Kultur (MWK) in the programme PROFILinternational, as well as the Spanish Ministry of Science and Innovation, co-funded by EU FEDER Funds, through grants RTI2018-095390-B-C33, PID2021-123673OB-C32 and TED2021-131295B-C33.Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.MDPI202320232023info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/27713reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/277132026-06-24T12:48:17Z
dc.title.none.fl_str_mv Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
title Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
spellingShingle Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
Bruns, Ralf
crowdsourcing
data stream learning
multiagent systems
collaborative coordination
market-based coordination
title_short Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
title_full Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
title_fullStr Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
title_full_unstemmed Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
title_sort Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
dc.creator.none.fl_str_mv Bruns, Ralf
Dötterl, Jeremias
Dunkel, Jürgen
Ossowski, Sascha
author Bruns, Ralf
author_facet Bruns, Ralf
Dötterl, Jeremias
Dunkel, Jürgen
Ossowski, Sascha
author_role author
author2 Dötterl, Jeremias
Dunkel, Jürgen
Ossowski, Sascha
author2_role author
author
author
dc.subject.none.fl_str_mv crowdsourcing
data stream learning
multiagent systems
collaborative coordination
market-based coordination
topic crowdsourcing
data stream learning
multiagent systems
collaborative coordination
market-based coordination
description This work was supported by the German Niedersächsisches Ministerium für Wissenschaftund Kultur (MWK) in the programme PROFILinternational, as well as the Spanish Ministry of Science and Innovation, co-funded by EU FEDER Funds, through grants RTI2018-095390-B-C33, PID2021-123673OB-C32 and TED2021-131295B-C33.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/27713
url https://hdl.handle.net/10115/27713
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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