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dc.contributor.authorJumadinova, Janyl
dc.contributor.authorDasgupta, Prithviraj
dc.contributor.authorSoh, Leen-Kiat
dc.date.accessioned2014-02-05T14:24:30Z
dc.date.available2014-02-05T14:24:30Z
dc.date.issued2013-11-01
dc.identifier.citationJumadinova, J., P. Dasgupta, and L. -K Soh. Strategic Capability-Learning for Improved Multiagent Collaboration in Ad Hoc Environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Vol. PP 2013.en_US
dc.identifier.issn2168-2216
dc.identifier.urihttp://hdl.handle.net/10456/35740
dc.description.abstractWe consider the problem of distributed collaboration among multiple agents in an ad hoc setting. We have analyzed this problem within a multiagent task execution scenario, in which every task requires collaboration among multiple agents to get completed. Tasks are also ad hoc in the sense that they appear dynamically and require different sets of expertise or capabilities from agents for completion. We model collaboration within this framework as a decision-making problem in which agents have to determine what capabilities to learn and from which agents to learn them so that they can form teams that have the capabilities required to perform the current tasks satisfactorily. Our proposed technique refers to principles from human learning theory to enable an agent to strategically select appropriate capabilities to learn from other agents. We also use two openness parameters to model the dynamic nature of tasks and agents in the environment. Experimental results within the Repast agent simulator show that by using the appropriate learning strategy, the overall utility of the agents improves considerably. The performance of the agents and their utilities are also dependent on the repetitiveness of tasks and reencounter with agents within the environment. Our results also show that the agents that are able to learn more capabilities from another expert agent outperform the agents who learn only one capability at a time from many agents, and agents who use an intelligent utility maximizing strategy to choose which capabilities to learn outperform the agents who randomly make the learning decision.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics: Systemsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSMC.2013.2285527en_US
dc.rights© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksen_US
dc.subjectAd hoc networksen_US
dc.subjectcollaborative worken_US
dc.subjectlearningen_US
dc.subjectmultiagent systemsen_US
dc.titleStrategic Capability-Learning for Improved Multiagent Collaboration in Ad Hoc Environmentsen_US
dc.description.versionPostprinten_US
dc.contributor.departmentComputer Scienceen_US
dc.identifier.doi10.1109/TSMC.2013.2285527
dc.contributor.avlauthorJumadinova, Janyl


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