Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach
This paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping funct...
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| Published in | Autonomous agents and multi-agent systems Vol. 22; no. 3; pp. 465 - 486 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.05.2011
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1387-2532 1573-7454 |
| DOI | 10.1007/s10458-010-9136-3 |
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| Abstract | This paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping functionality regarding the execution of each task but differing levels of capability; and some tasks may depict constraints such as simultaneous execution. So far these four characteristics have not been fully tested in domains such as the RoboCup Rescue. We use a swarm intelligence based approach, address all characteristics, and compare it to other two GAP-based algorithms. Experiments where computational effort, communication load, and the score obtained in the RoboCup Rescue aremeasured, show that our approach outperforms the others. |
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| AbstractList | This paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping functionality regarding the execution of each task but differing levels of capability; and some tasks may depict constraints such as simultaneous execution. So far these four characteristics have not been fully tested in domains such as the RoboCup Rescue. We use a swarm intelligence based approach, address all characteristics, and compare it to other two GAP-based algorithms. Experiments where computational effort, communication load, and the score obtained in the RoboCup Rescue aremeasured, show that our approach outperforms the others. |
| Author | dos Santos, Fernando Bazzan, Ana L. C. |
| Author_xml | – sequence: 1 givenname: Fernando surname: dos Santos fullname: dos Santos, Fernando organization: PPGC—Universidade Federaldo Rio Grande do Sul – sequence: 2 givenname: Ana L. C. surname: Bazzan fullname: Bazzan, Ana L. C. email: bazzan@inf.ufrgs.br organization: PPGC—Universidade Federaldo Rio Grande do Sul |
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| CitedBy_id | crossref_primary_10_1016_j_artint_2013_12_002 crossref_primary_10_1016_j_robot_2024_104722 crossref_primary_10_1527_tjsai_39_6_AG24_F crossref_primary_10_1016_j_physa_2017_08_094 crossref_primary_10_1609_aimag_v33i3_2420 crossref_primary_10_1007_s11721_020_00181_3 crossref_primary_10_1007_s13218_016_0444_x crossref_primary_10_3390_math11194124 crossref_primary_10_1145_3303848 crossref_primary_10_1007_s11370_021_00372_9 crossref_primary_10_1007_s00500_017_2839_5 crossref_primary_10_1016_j_physa_2019_02_007 crossref_primary_10_3390_a17090379 crossref_primary_10_1017_S0269888920000235 |
| Cites_doi | 10.1016/S0921-8890(99)00066-4 10.1145/1082473.1082584 10.1016/j.artint.2007.03.002 10.1098/rspb.1998.0299 10.1023/B:AGNT.0000018807.12771.60 10.1007/BF00354978 10.1038/35023164 10.1007/11780519_63 10.1109/TC.1980.1675516 10.2307/2387648 10.1007/s10458-009-9087-8 10.1023/A:1020859531179 10.1109/ICMAS.2000.858447 10.1093/oso/9780195131581.001.0001 |
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| Keywords | Optimisation in multiagent systems Swarm intelligence Task allocation Robocup Rescue |
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| References_xml | – reference: KubeR.BonabeauE.Cooperative transport by ants and robotsRobotics and Autonomous Systems2000301/28510110.1016/S0921-8890(99)00066-4ISSN: 0921-8890 – reference: HamM.AghaG.Market-based coordination strategies for large-scale multi-agent systemsSystem and Information Sciences Notes200721126131 – reference: RobsonS. K.TranielloJ. F. A.Resource assessment, recruitment behavior, and organization of cooperative prey retrieval in the ant Formica schaufussi (hymenoptera: Formicidae)Journal of Insect Behavior199811112210.1023/A:1020859531179 – reference: BonabeauE.TheraulazG.DorigoM.Swarm intelligence: From natural to artificial systems1999New York, USAOxford University Press1003.68123 – reference: KriegerM. J. B.BilleterJ.-B.KellerL.Ant-like task allocation and recruitment in cooperative robotsNature2000406679999299510.1038/35023164 – reference: ScerriP.FarinelliA.OkamotoS.TambeM.DignumF.DignumV.KoenigS.KrausS.SinghM.P.WooldridgeM.Allocating tasks in extreme teamsProceedings of the fourth international joint conference on autonomous agents and multiagent systems2005New York USAACM Press72773410.1145/1082473.1082584 – reference: HamM.AghaG.JamaliN.ScerriP.SugawaraT.A study of coordinated dynamic market-based task assignment in massively multi-agent systemsMassively multiagent systems, volume of 5043 in lecture notes in artificial intelligence2008BerlinSpringer4363 – reference: Santos, F. D., & Bazzan, A. L. C. (2009). eXtreme-ants: Ant based algorithm for task allocation in extreme teams. In N. R. Jennings, A. Rogers, J. A. R. Aguilar, A. Farinelli, & S. D. Ramchurn (Eds.), Proceedings of the second international workshop on optimisation in multi-agent systems (pp. 1–8). Budapest, Hungary, May. – reference: Hunsberger, L., & Grosz, B. J. (2000). A combinatorial auction for collaborative planning. In Proceedings of the fourth international conference on multiAgent aystems, ICMAS (pp. 151–158). Boston. – reference: FerreiraP. R.Jr.BoffoF.BazzanA. L. C.JamaliN.ScerriP.SugawaraT.Using swarm-GAP for distributed task allocation in complex scenariosMassively multiagent systems, volume of 5043 in lecture notes in artificial intelligence2008BerlinSpringer107121 – reference: Shehory, O., & Kraus, S. (1995). Task allocation via coalition formation among autonomous agents. In Proceedings of the fourteenth international joint conference on artificial intelligence (pp. 655–661). Montréal, Canada: Morgan Kaufmann. – reference: Xu, Y., Scerri, P., Sycara, K., & Lewis, M. (2006). Comparing market and token-based coordination. In Proceedings of the fifth international joint conference on autonomous agents and multiagent systems, AAMAS 2006 (pp. 1113–1115). New York, NY, USA: ACM. – reference: CicirelloV.SmithS.Wasp-like agents for distributed factory coordinationJournal of Autonomous Agents and Multi-Agent Systems20048323726610.1023/B:AGNT.0000018807.12771.60 – reference: SkinnerC.BarleyM.BredenfeldA.JacoffA.NodaI.TakahashiY.Robocup Rescue simulation competition: Status reportRoboCup 2005: Robot Soccer world cup IX, volume of 4020 in lecture notes in computer science2006BerlinSpringer63263910.1007/11780519_63 – reference: Agassounon, W., & Martinoli, A. (2002). Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In Proceedings of the first international joint conference on autonomous agents and multiagent systems, AAMAS 2002 (pp. 1090–1097). New York, NY, USA: ACM. – reference: Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., & Shimada, S. (1999). Robocup Rescue: Search and rescue in large-scale disasters as adomain for autonomous agents research. In Proceedings of the IEEE international conference on systems, man, and cybernetics, SMC (Vol. 6, pp. 739–743) Tokyo, Japan: IEEE. – reference: FerreiraP. R.Jr.dos SantosF.BazzanA. L. C.EpsteinD.WaskowS. J.Robocup Rescue as multiagent task allocation among teams: Experiments with task interdependenciesJournal of Autonomous Agents and Multiagent Systems201020342144310.1007/s10458-009-9087-8 – reference: HölldoblerB.Territorial behavior in the green tree ant (Oecophylla smaragdina)Biotropica198315424125010.2307/2387648 – reference: Theraulaz, G., Bonabeau, E., & Deneubourg, J. (1998). Response threshold reinforcement and division of labour in insect societies. In Royal Society of London Series B - Biological Sciences, 265, 327–332. – reference: SmithR.G.The contract net protocol: High-level communication and control in a distributed problem solverIEEE Transactions on Computers1980C-29121104111310.1109/TC.1980.1675516 – reference: RahwanT.JenningsN. R.An algorithm for distributing coalitional value calculations among cooperating agentsArtificial Intelligence Journal20071718–953556710.1016/j.artint.2007.03.0021168.685882333553 – reference: HölldoblerB.StantonR. C.MarklH.Recruitment and food-retrieving behavior in Novomessor (formicidae, hymenoptera)Behavioral Ecology and Sociobiology19784216318110.1007/BF00354978 – ident: 9136_CR22 – volume: 2 start-page: 126 issue: 1 year: 2007 ident: 9136_CR6 publication-title: System and Information Sciences Notes – start-page: 43 volume-title: Massively multiagent systems, volume of 5043 in lecture notes in artificial intelligence year: 2008 ident: 9136_CR7 – volume: 30 start-page: 85 issue: 1/2 year: 2000 ident: 9136_CR13 publication-title: Robotics and Autonomous Systems doi: 10.1016/S0921-8890(99)00066-4 – ident: 9136_CR16 – start-page: 727 volume-title: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems year: 2005 ident: 9136_CR17 doi: 10.1145/1082473.1082584 – ident: 9136_CR1 – volume: 171 start-page: 535 issue: 8–9 year: 2007 ident: 9136_CR14 publication-title: Artificial Intelligence Journal doi: 10.1016/j.artint.2007.03.002 – ident: 9136_CR21 doi: 10.1098/rspb.1998.0299 – volume: 8 start-page: 237 issue: 3 year: 2004 ident: 9136_CR3 publication-title: Journal of Autonomous Agents and Multi-Agent Systems doi: 10.1023/B:AGNT.0000018807.12771.60 – ident: 9136_CR18 – volume: 4 start-page: 163 issue: 2 year: 1978 ident: 9136_CR9 publication-title: Behavioral Ecology and Sociobiology doi: 10.1007/BF00354978 – volume: 406 start-page: 992 issue: 6799 year: 2000 ident: 9136_CR12 publication-title: Nature doi: 10.1038/35023164 – start-page: 632 volume-title: RoboCup 2005: Robot Soccer world cup IX, volume of 4020 in lecture notes in computer science year: 2006 ident: 9136_CR19 doi: 10.1007/11780519_63 – start-page: 107 volume-title: Massively multiagent systems, volume of 5043 in lecture notes in artificial intelligence year: 2008 ident: 9136_CR4 – ident: 9136_CR11 – volume: C-29 start-page: 1104 issue: 12 year: 1980 ident: 9136_CR20 publication-title: IEEE Transactions on Computers doi: 10.1109/TC.1980.1675516 – volume: 15 start-page: 241 issue: 4 year: 1983 ident: 9136_CR8 publication-title: Biotropica doi: 10.2307/2387648 – volume: 20 start-page: 421 issue: 3 year: 2010 ident: 9136_CR5 publication-title: Journal of Autonomous Agents and Multiagent Systems doi: 10.1007/s10458-009-9087-8 – volume: 11 start-page: 1 issue: 1 year: 1998 ident: 9136_CR15 publication-title: Journal of Insect Behavior doi: 10.1023/A:1020859531179 – ident: 9136_CR10 doi: 10.1109/ICMAS.2000.858447 – volume-title: Swarm intelligence: From natural to artificial systems year: 1999 ident: 9136_CR2 doi: 10.1093/oso/9780195131581.001.0001 |
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| Title | Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach |
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