Show simple item record

Mobilized ad-hoc networks: A reinforcement learning approach

dc.date.accessioned2005-12-22T01:15:17Z
dc.date.accessioned2018-11-24T10:23:58Z
dc.date.available2005-12-22T01:15:17Z
dc.date.available2018-11-24T10:23:58Z
dc.date.issued2003-12-04
dc.identifier.urihttp://hdl.handle.net/1721.1/30437
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/30437
dc.description.abstractResearch in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. Reinforcement learning methods can be used tocontrol both packet routing decisions and node mobility, dramaticallyimproving the connectivity of the network. We first motivate theproblem by presenting theoretical bounds for the connectivityimprovement of partially mobile networks and then present superiorempirical results under a variety of different scenarios in which themobile nodes in our ad-hoc network are embedded with adaptive routingpolicies and learned movement policies.
dc.format.extent9 p.
dc.format.extent15523730 bytes
dc.format.extent577014 bytes
dc.language.isoen_US
dc.subjectAI
dc.subjectreinforcement learning
dc.subjectmulti-agent learning
dc.subjectad-hoc networking
dc.titleMobilized ad-hoc networks: A reinforcement learning approach


Files in this item

FilesSizeFormatView
MIT-CSAIL-TR-2003-032.pdf577.0Kbapplication/pdfView/Open
MIT-CSAIL-TR-2003-032.ps15.52Mbapplication/postscriptView/Open

This item appears in the following Collection(s)

Show simple item record