Benjamin Klöpper, Wilhelm Dangelmaier


The paradigm of self-optimization introduces flexible and highly adaptive mechatronic systems. During the exploiation of this flexibility, new problems arise. One of these problems is the coordination of mechatronics systems and subsystems. This paper introduces the application area self-optimizing mechatronic systems and identifies the arising coordination problems. Two main scenarios are identified: coordination of autonomous mechatronic systems and coordination of several subsystems within an autonomous mechatronic system. We will show that multi-agent technology and in particular multi-agent planning can be applied to solve both coordination scenarios.


  1. Al-Safi, Y. and Vyatkin, V. (2007). An ontology-based reconfiguration agent for intelligent mechatronic systems. In Holonic and Multi-Agent Systems for Manufacturing, number 4569 in Lecture Notes In Artificial Intelligence, Berlin. Springer Verlag.
  2. Baum, W., Bredenfeld, A., Hans, M., Hertzberg, J., Ritter, A., Schonherr, F., Christaller, T., and Schraft, R. (2002). Integrating heterogeneous robot and software components by agent technology. In Robotik 2002, volume 1679 of VDI-Berichte. VDI Verlag GmbH.
  3. Bradley, D. (1997). The what, why and how of mechatronics. Engineering Science and Education Journal, 6(2):81-88.
  4. Conitzer, V. (2008). Comparing multiagent systems research in combinatorial auctions and voting. In 10th International Symposium on Artificial Intelligence and Mathematics (ISAIM-08).
  5. Danne, C., Dück, V., and Klöpper, B. (2006). Selfish motivated cooperative planning in cross networked mechatronic system. International Transactions on Systems Science and Applications, 2(2):221-225.
  6. David, E., Azoulay-Schwartz, R., and Kraus, S. (2002). Protocols and strategies for automated multi-attribute auctions. In Proc. First Joint Conf. on Autonomous Agents and Multiagent Systems, pages 77-85.
  7. Dürksen, D., Klöpper, B., Ruth, D., and Thonemann, C. (2008). Combining distributed matchmaking and clustering to prune the solution space in distributed optimization problems - demonstrated in the railcab system. In Eighth International Conference on Hybrid Intelligent Systems, pages 108-113,, Barcelona. IEEE Computer Society, IEEE Computer Society Press.
  8. Faratin, P., Sierra, C., and Jennings, N. R. (1998). Negotiation decision functions for autonomous agents. International Journal of Robotics and Autonomous Systems, 24:3-4.
  9. Gausemeier, J., Kahl, S., and Pook, S. (2008). From mechatronics to self-optimizing systems. In 7. Internationales Heinz Nixdorf Symposium, volume 223 of HNI-Verlagsschriftenreihe, Paderborn. Heinz Nixdorf Institut, HNI Verlagsschriftenreihe, Paderborn.
  10. Gerevini, A. and Long, D. (2005). Plan constraints and preferences in pddl3. Technical report, Department of Electronics for Automation, University of Brescia.
  11. Kirk, D. E. (1970). Optimal control theory. Prentice-Hall, Englewood Cliffs, NJ.
  12. Klöpper, B., Romaus, C., Schmidt, A., and Vöcking, H. (2008a). A multi-agent planning problem for the coordination of functions modules. In Self-Optimizing Mechatronic Systems: Design the Future.
  13. Klöpper, B., Romaus, C., Schmidt, A., V öcking, H., and Donoth, J. (2008b). Defining a system of objectives for multi-agent coordination of function modules within mechatronic systems. In : Proceedings of IDETC/CIE 2008 ASME 2008 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, volume . uk, uk.
  14. Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Neimann, D., Prassad, M. N., Raja, A., Vincent, R., Xuan, P., and Zhang, X. (2004). Evolution of the gpgp/taems domain-independent coordination framework. Autonomous Agents and Multi-Agent Systems, 9:87-143.
  15. Ogston, E. and Vassiliadis, S. (2002). Unstructured agent matchmaking: experiments in timing and fuzzy matching. In SAC 7802: Proceedings of the 2002 ACM symposium on Applied computing, pages 300-305, New York, NY, USA. ACM.
  16. Pahl, G. and Beitz, W. (07). Engineering Design: A Systematic Approach. Springer.
  17. Russell, S. J. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Pearson Education.
  18. Sycara, K., Decker, K., and Williamson, M. (1997). Middle-agents for the internet. In Proceedings of IJCAI-97.
  19. (VDI), V. D. I. (2004). Vdi-guideline 2206 - design methodology for mechatronic systems. Technical report, Verein Deutscher Ingenieure (VDI), Beuth-Verlag, Berlin.
  20. Weerdt, M., Mors, A., and Witteveen, C. (2005). Multiagent planning - an introduction to planning and coordination. Technical report, Dept. of Software Technology, Delft University of Technology.
  21. Witting, K., Schulz, B., Dellnitz, M., Bcker, J., and Frhlecke, N. (2008). A new approach for online multiobjective optimization of mechatronic systems. International Journal on Software Tools for Technology Transfer STTT, 10:223-231.
  22. Wooldridge, M. and Jennings, N. R. (1998). Formalizing the cooperative problem solving process, pages 430- 440. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Paper Citation

in Harvard Style

Klöpper B. and Dangelmaier W. (2009). COORDINATION OF SELF-OPTIMIZING MECHATRONIC SYSTEMS - A New Application for Multi-Agent Planning . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 312-317. DOI: 10.5220/0001794903120317

in Bibtex Style

author={Benjamin Klöpper and Wilhelm Dangelmaier},
title={COORDINATION OF SELF-OPTIMIZING MECHATRONIC SYSTEMS - A New Application for Multi-Agent Planning},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
SN - 978-989-8111-66-1
AU - Klöpper B.
AU - Dangelmaier W.
PY - 2009
SP - 312
EP - 317
DO - 10.5220/0001794903120317