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Authors: Mihail Mihaylov 1 ; Yann-Aël Le Borgne 1 ; Ann Nowé 1 and Karl Tuyls 2

Affiliations: 1 Vrije Universiteit Brussel, Belgium ; 2 Maastricht University, Netherlands

Keyword(s): Reinforcement learning, Synchronicity and Desynchronicity, Wireless sensor networks, Wake-up scheduling.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Collective Intelligence ; Cooperation and Coordination ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Technologies ; Multi-Agent Systems ; Operational Research ; Simulation ; Software Engineering ; Symbolic Systems

Abstract: We present a self-organizing reinforcement learning (RL) approach for coordinating the wake-up cycles of nodes in a wireless sensor network in a decentralized manner. To the best of our knowledge we are the first to demonstrate how global synchronicity and desynchronicity can emerge through local interactions alone without the need of central mediator or any form of explicit coordination. We apply this RL approach to wireless sensor nodes arranged in different topologies and study how agents, starting with a random policy, are able to self-adapt their behavior based only on their interaction with neighboring nodes. Each agent independently learns to which nodes it should synchronize to improve message throughput and at the same with whom to desynchronize in order to reduce communication interference. The obtained results show how simple and computationally bounded sensor nodes are able to coordinate their wake-up cycles in a distributed way in order to improve the global system perfo rmance through (de)synchronicity. (More)

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Paper citation in several formats:
Mihaylov, M.; Le Borgne, Y.; Nowé, A. and Tuyls, K. (2011). SELF-ORGANIZING SYNCHRONICITY AND DESYNCHRONICITY USING REINFORCEMENT LEARNING. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-8425-41-6; ISSN 2184-433X, SciTePress, pages 94-103. DOI: 10.5220/0003162600940103

@conference{icaart11,
author={Mihail Mihaylov. and Yann{-}Aël {Le Borgne}. and Ann Nowé. and Karl Tuyls.},
title={SELF-ORGANIZING SYNCHRONICITY AND DESYNCHRONICITY USING REINFORCEMENT LEARNING},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2011},
pages={94-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003162600940103},
isbn={978-989-8425-41-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - SELF-ORGANIZING SYNCHRONICITY AND DESYNCHRONICITY USING REINFORCEMENT LEARNING
SN - 978-989-8425-41-6
IS - 2184-433X
AU - Mihaylov, M.
AU - Le Borgne, Y.
AU - Nowé, A.
AU - Tuyls, K.
PY - 2011
SP - 94
EP - 103
DO - 10.5220/0003162600940103
PB - SciTePress

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