A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks

Sydney Australia November 28-December 01

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http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.25

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	Wilhelm Dangelmaier, Tobias Rust, Andre Doring, Benjamin Klopper, "A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks," Computational Intelligence for Modelling, Control and Automation, International Conference on, pp. 84, International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06), 2006.

	BibTex	x
	@article{ 10.1109/CIMCA.2006.25, author = {Wilhelm Dangelmaier and Tobias Rust and Andre Doring and Benjamin Klopper}, title = {A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks}, journal ={Computational Intelligence for Modelling, Control and Automation, International Conference on}, volume = {0}, year = {2006}, isbn = {0-7695-2731-0}, pages = {84}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.25}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computational Intelligence for Modelling, Control and Automation, International Conference on TI - A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks SN - 0-7695-2731-0 SP EP A1 - Wilhelm Dangelmaier, A1 - Tobias Rust, A1 - Andre Doring, A1 - Benjamin Klopper, PY - 2006 KW - null VL - 0 JA - Computational Intelligence for Modelling, Control and Automation, International Conference on ER -

Wilhelm Dangelmaier, University of Paderborn, Germany

Tobias Rust, University of Paderborn, Germany

Andre Doring, University of Paderborn, Germany

Benjamin Klopper, University of Paderborn, Germany

In production networks companies need fast reactions due to changes of supply and demand. To realize such a change management in an effective way the involved companies have to synchronize their quantities and capacities collaboratively. For these purposes the multiagent system MASCOPP was developed at the Heinz Nixdorf Institute, which tries to eliminate conflicts in a production network, based on changes of plans, through bilateral communication between the involved companies. Human experts have to configure the system by creating coordination rules to solve the conflicts. In this paper we introduce a machine learning concept to learn these coordination rules objectively by a reinforcement learning approach.

Citation:

Wilhelm Dangelmaier, Tobias Rust, Andre Doring, Benjamin Klopper, "A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks," cimca, pp.84, International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06), 2006

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