Mobilized ad-hoc networks: A reinforcement learning approach

dc.creator	Chang, Yu-Han
dc.creator	Ho, Tracey
dc.creator	Kaelbling, Leslie Pack
dc.date	2004-10-08T20:43:04Z
dc.date	2004-10-08T20:43:04Z
dc.date	2003-12-04
dc.date.accessioned	2013-10-09T02:46:34Z
dc.date.available	2013-10-09T02:46:34Z
dc.date.issued	2013-10-09
dc.identifier	AIM-2003-025
dc.identifier	http://hdl.handle.net/1721.1/6732
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Research 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.
dc.format	9 p.
dc.format	771382 bytes
dc.format	1199447 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-2003-025
dc.subject	AI
dc.subject	reinforcement learning
dc.subject	multi-agent learning
dc.subject	ad-hoc networking
dc.title	Mobilized ad-hoc networks: A reinforcement learning approach

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