An autonomic routing framework for sensor networks
Cluster Computing
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Perspectives on multiagent learning
Artificial Intelligence
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
Power consumption optimization and delay minimization in MANET
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
KEPPAN: Knowledge exploitation for proactively-planned ad-hoc networks
Journal of Network and Computer Applications
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An enhanced reinforcement routing protocol for inter-vehicular unicast application
EuroIMSA '08 Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications
Using graph analysis to study networks of adaptive agent
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Cooperative adaptive spectrum sharing in cognitive radio networks
IEEE/ACM Transactions on Networking (TON)
Video streaming application over WEAC protocol in MANET
Journal of Computer and System Sciences
A dynamic route change mechanism for mobile ad hoc networks
International Journal of Communication Networks and Distributed Systems
Journal of Network and Computer Applications
Wireless Personal Communications: An International Journal
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With the cost of wireless networking and computational power rapidly dropping, mobile ad-hoc networks will soon become an important part of our societyýs computing structures. While there is a great deal of research from the networking community regarding the routing of information over such networks, most of these techniques lack automatic adaptivity. The size and complexity of these networks demand that we apply the principles of autonomic computing to this problem. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We present two applications of reinforcement learning methods to the mobilized ad-hoc networking domain and demonstrate some promising empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with these adaptive routing policies and learned movement policies.