Stream-based perception for cognitive agents in mobile ecosystems

Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user’s current situation. Unfortunately, today’s cognitive agent frameworks cannot cope well with the challenging ch...

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Detalles Bibliográficos
Autores: Dötterl, Jeremias, Bruns, Ralf, Dunkel, Jürgen, Ossowski, Sascha
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/28550
Acceso en línea:https://hdl.handle.net/10115/28550
Access Level:acceso abierto
Palabra clave:Multi-agent systems
data stream processing
mobile computing
Agent perception
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spelling Stream-based perception for cognitive agents in mobile ecosystemsDötterl, JeremiasBruns, RalfDunkel, JürgenOssowski, SaschaMulti-agent systemsdata stream processingmobile computingAgent perceptionCognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user’s current situation. Unfortunately, today’s cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on high-level percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.IOS Press202420242019info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/28550reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution-NonCommercial-NoDerivs 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/285502026-06-24T12:48:17Z
dc.title.none.fl_str_mv Stream-based perception for cognitive agents in mobile ecosystems
title Stream-based perception for cognitive agents in mobile ecosystems
spellingShingle Stream-based perception for cognitive agents in mobile ecosystems
Dötterl, Jeremias
Multi-agent systems
data stream processing
mobile computing
Agent perception
title_short Stream-based perception for cognitive agents in mobile ecosystems
title_full Stream-based perception for cognitive agents in mobile ecosystems
title_fullStr Stream-based perception for cognitive agents in mobile ecosystems
title_full_unstemmed Stream-based perception for cognitive agents in mobile ecosystems
title_sort Stream-based perception for cognitive agents in mobile ecosystems
dc.creator.none.fl_str_mv Dötterl, Jeremias
Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author Dötterl, Jeremias
author_facet Dötterl, Jeremias
Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author_role author
author2 Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author2_role author
author
author
dc.subject.none.fl_str_mv Multi-agent systems
data stream processing
mobile computing
Agent perception
topic Multi-agent systems
data stream processing
mobile computing
Agent perception
description Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user’s current situation. Unfortunately, today’s cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on high-level percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/28550
url https://hdl.handle.net/10115/28550
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivs 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
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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