Autonomic multi-policy optimization in pervasive systems: Overview and evaluation

@article{Dusparic2012AutonomicMO,
  title={Autonomic multi-policy optimization in pervasive systems: Overview and evaluation},
  author={Ivana Dusparic and Vinny Cahill},
  journal={TAAS},
  year={2012},
  volume={7},
  pages={11:1-11:25}
}
This article describes Distributed W-Learning (DWL), a reinforcement learning-based algorithm for collaborative agent-based optimization of pervasive systems. DWL supports optimization towards multiple heterogeneous policies and addresses the challenges arising from the heterogeneity of the agents that are charged with implementing them. DWL learns and exploits the dependencies between agents and between policies to improve overall system performance. Instead of always executing the locally… CONTINUE READING
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