Elastic Scheduling for Flexible Workload Management
IEEE Transactions on Computers
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Real Time Scheduling Theory: A Historical Perspective
Real-Time Systems
Fair Bandwidth Sharing in Distributed Systems: A Game-Theoretic Approach
IEEE Transactions on Computers
Quantifying the impact of learning algorithm parameter tuning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Self-organizing Bandwidth Sharing in Priority-Based Medium Access
SASO '09 Proceedings of the 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems
ISORCW '10 Proceedings of the 2010 13th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops
Proceedings of the Conference on Design, Automation and Test in Europe
IEEE Transactions on Intelligent Transportation Systems
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Introduction to the Special Issue: SORT 2010
Concurrency and Computation: Practice & Experience
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The raising complexity in distributed embedded systems makes it necessary that the communication of such systems organizes itself automatically. In this paper, we tackle the problem of sharing bandwidth on priority-based buses. Based on a game theoretic model, reinforcement learning algorithms are proposed that use simple local rules to establish bandwidth sharing. The algorithms require little computational effort and no additional communication. Extensive experiments show that the proposed algorithms establish the desired properties without global knowledge ortextita priori information. It is proven that communication nodes using these algorithms can co-exist with nodes using other scheduling techniques. Finally, we propose a procedure that helps to set the learning parameters according to the desired behavior. Copyright © 2011 John Wiley & Sons, Ltd.