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Mobilized ad-hoc networks: A reinforcement learning approach

dc.date.accessioned2004-10-08T20:43:04Z
dc.date.accessioned2018-11-24T10:21:42Z
dc.date.available2004-10-08T20:43:04Z
dc.date.available2018-11-24T10:21:42Z
dc.date.issued2003-12-04en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6732
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6732
dc.description.abstractResearch in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We first motivate the problem by presenting theoretical bounds for the connectivity improvement of partially mobile networks and then present superior empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with adaptive routing policies and learned movement policies.en_US
dc.format.extent9 p.en_US
dc.format.extent771382 bytes
dc.format.extent1199447 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectreinforcement learningen_US
dc.subjectmulti-agent learningen_US
dc.subjectad-hoc networkingen_US
dc.titleMobilized ad-hoc networks: A reinforcement learning approachen_US


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