Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPel - Guaiaca |
Texto Completo: | http://guaiaca.ufpel.edu.br/handle/123456789/75 |
Resumo: | This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. It is well known that DCOP, when used to model complex scenarios, generates problems with exponentially growing number of parameters. However, those scenarios are becoming ubiquitous in real-world applications. Therefore, approximate solutions are necessary. We propose and evaluate an algorithm for distributed task allocation. This algorithm, called Swarm-GAP, is based on theoretical models of division of labor in social insect colonies. It uses a probabilistic decision model. Swarm-GAP is experimented both in a scenario from RoboCup Rescue and an abstract simulation environment. We show that Swarm-GAP achieves similar results as other recent proposed algorithm with a reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks. |
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Ferreira Júnior, Paulo RobertoBazzan, Ana Lúcia CetertichBoffo, F. S.2010-09-29T13:23:00Z2010-09-29T13:23:00Z2008-08-17FERREIRA JÚNIOR, Paulo Roberto ; BAZZAN, Ana Lúcia Cetertich ; BOFFO, F. S. Using Swarm-GAP for distributed task allocation in complex scenarios. Lecture Notes in Computer Science, v. 5043, p. 107-121, 2008.http://guaiaca.ufpel.edu.br/handle/123456789/75This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. It is well known that DCOP, when used to model complex scenarios, generates problems with exponentially growing number of parameters. However, those scenarios are becoming ubiquitous in real-world applications. Therefore, approximate solutions are necessary. We propose and evaluate an algorithm for distributed task allocation. This algorithm, called Swarm-GAP, is based on theoretical models of division of labor in social insect colonies. It uses a probabilistic decision model. Swarm-GAP is experimented both in a scenario from RoboCup Rescue and an abstract simulation environment. We show that Swarm-GAP achieves similar results as other recent proposed algorithm with a reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks.Springer Berlin / HeidelbergDistributed task allocation. Swarm intelligence. Multiagent sstems.Using Swarm-GAP for Distributed Task Allocation in Complex Scenariosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleengreponame:Repositório Institucional da UFPel - Guaiacainstname:Universidade Federal de Pelotas (UFPEL)instacron:UFPELinfo:eu-repo/semantics/openAccessORIGINALartigo_02.pdfartigo_02.pdfapplication/pdf242842http://guaiaca.ufpel.edu.br/xmlui/bitstream/123456789/75/1/artigo_02.pdf141972203711e223c08ae4fc8811fed5MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://guaiaca.ufpel.edu.br/xmlui/bitstream/123456789/75/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52open accessTEXTartigo_02.pdf.txtartigo_02.pdf.txtExtracted Texttext/plain36464http://guaiaca.ufpel.edu.br/xmlui/bitstream/123456789/75/3/artigo_02.pdf.txtd859f9130a69a5d8020ef053f8c22762MD53open accessTHUMBNAILartigo_02.pdf.jpgartigo_02.pdf.jpgGenerated Thumbnailimage/jpeg1375http://guaiaca.ufpel.edu.br/xmlui/bitstream/123456789/75/4/artigo_02.pdf.jpg3c72a0be628bacbc2a3f2265ce8c8d4dMD54open access123456789/752020-07-03 17:47:58.73open accessoai:guaiaca.ufpel.edu.br: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Repositório InstitucionalPUBhttp://repositorio.ufpel.edu.br/oai/requestrippel@ufpel.edu.br || repositorio@ufpel.edu.br || aline.batista@ufpel.edu.bropendoar:2020-07-03T20:47:58Repositório Institucional da UFPel - Guaiaca - Universidade Federal de Pelotas (UFPEL)false |
dc.title.pt_BR.fl_str_mv |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
title |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
spellingShingle |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios Ferreira Júnior, Paulo Roberto Distributed task allocation. Swarm intelligence. Multiagent sstems. |
title_short |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
title_full |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
title_fullStr |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
title_full_unstemmed |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
title_sort |
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios |
author |
Ferreira Júnior, Paulo Roberto |
author_facet |
Ferreira Júnior, Paulo Roberto Bazzan, Ana Lúcia Cetertich Boffo, F. S. |
author_role |
author |
author2 |
Bazzan, Ana Lúcia Cetertich Boffo, F. S. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Ferreira Júnior, Paulo Roberto Bazzan, Ana Lúcia Cetertich Boffo, F. S. |
dc.subject.por.fl_str_mv |
Distributed task allocation. Swarm intelligence. Multiagent sstems. |
topic |
Distributed task allocation. Swarm intelligence. Multiagent sstems. |
description |
This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. It is well known that DCOP, when used to model complex scenarios, generates problems with exponentially growing number of parameters. However, those scenarios are becoming ubiquitous in real-world applications. Therefore, approximate solutions are necessary. We propose and evaluate an algorithm for distributed task allocation. This algorithm, called Swarm-GAP, is based on theoretical models of division of labor in social insect colonies. It uses a probabilistic decision model. Swarm-GAP is experimented both in a scenario from RoboCup Rescue and an abstract simulation environment. We show that Swarm-GAP achieves similar results as other recent proposed algorithm with a reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks. |
publishDate |
2008 |
dc.date.issued.fl_str_mv |
2008-08-17 |
dc.date.accessioned.fl_str_mv |
2010-09-29T13:23:00Z |
dc.date.available.fl_str_mv |
2010-09-29T13:23:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
FERREIRA JÚNIOR, Paulo Roberto ; BAZZAN, Ana Lúcia Cetertich ; BOFFO, F. S. Using Swarm-GAP for distributed task allocation in complex scenarios. Lecture Notes in Computer Science, v. 5043, p. 107-121, 2008. |
dc.identifier.uri.fl_str_mv |
http://guaiaca.ufpel.edu.br/handle/123456789/75 |
identifier_str_mv |
FERREIRA JÚNIOR, Paulo Roberto ; BAZZAN, Ana Lúcia Cetertich ; BOFFO, F. S. Using Swarm-GAP for distributed task allocation in complex scenarios. Lecture Notes in Computer Science, v. 5043, p. 107-121, 2008. |
url |
http://guaiaca.ufpel.edu.br/handle/123456789/75 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Berlin / Heidelberg |
publisher.none.fl_str_mv |
Springer Berlin / Heidelberg |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFPel - Guaiaca instname:Universidade Federal de Pelotas (UFPEL) instacron:UFPEL |
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Universidade Federal de Pelotas (UFPEL) |
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UFPEL |
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UFPEL |
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Repositório Institucional da UFPel - Guaiaca |
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Repositório Institucional da UFPel - Guaiaca |
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