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Integrating skills into multi-agent systems

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Abstract

Currently, an important topic of robotic research is the design and development of multi-agent robot systems (MASs). In these a number of autonomous robots cooperate and coordinate themselves in order to pursue given goals. The agents of an MAS not only have to work autonomously or in cooperation with other agents, but in dynamic, relatively unstructured environments. Therefore, the agents require agent-specific but flexible skills to cope with their tasks and the environment's variability. On the other hand, the actions to be performed by agents in an MAS have to meet certain requirements imposed by the MAS's structure. The representation of actions has to support planning, inter-agent communication, task negotiation etc. In this paper, we describe a method of combining the agent-specific nature of skills with the requirements for a general action knowledge representation inherent to MASs, by presenting elementary operations (EOs) that provide an appropriate interface.

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References

  • Baroglio, C., Giordana, A. and Piola, R. (1994) Learning control functions for industrial robots, in Proceedings of the ML-94 work-shop on Robot Learning New Brunswick.

  • Gullapalli, V., Franklin, J. A. and Benbrahim, H. (1994) Ac-quiring robot skills via reinforcement learning. IEEE Control systems Magazine, 14(1), 13–24.

    Google Scholar 

  • Hirzinger, G. (1993) ROTEX-the first robot in space, in Pro-ceedings of the International Conference on Advanced Robotics (ICAR '93), Tokyo, Japan, pp. 9–13.

  • Kaiser, M., Rogalla, O. and Dillmann, R. (1996a) Communica-tion as the basis for learning in multi-agent systems, in ECAI '96 Workshop on Learning in Distributed AI Systems, Weiss, G. (ed.), Budapest, Hungary, pp. 50–59.

  • Kaiser, M., Dillmann, R., Friedrich, H., Lin, I., Wallner, F. and Weckesser, P. (1996b) Learning coordination skills in multi-agent systems, in IEEE/RSJ International Conference on In-telligent Robots and Systems, Osaka, vol. 3, pp. 1488–1495.

    Google Scholar 

  • Kaiser, M., Klingspor, V., Millàn, J. del R., Accame, M., Wall-ner, F. and Dillmann, R. (1995) Achieving intelligence in mobility-incorporating learning capabilities in realworld mobile robots. Technical report, University of Dortmund.

  • Kosuge, K., Takeo, K., Fukuda, T., Sugiura, T., Sakai, A. and Yamada, K. (1994) Uni fied approach for teleoperation of virtual and real environment for skill based teleoperation, in Proceedings of the IEEE/RSJ Conference on Intelligent Ro-bots and Systems, Munich, Germany, pp. 1242–1247.

  • Kreuziger, J. and Hauser, M. (1993) A new system architecture for applying symbolic learning techniques to robot manipulation, in Proceedings of the IEEE/RSJ Conference on Intel-ligent Robots and Systems, Yokohama, Japan, pp. 1441–1448.

  • Längle, Th., Lüth, T. C. and Rembold, U. (1995) A distributed control architecture for autonomous robot systems, in Modelling and Planning for Sensor Based Intelligent Robot Systems, Bunke, H., Noltemeier, H. and Kanade, T. (eds), World Scientific, Singapore, New Jersey, London, Hong Kong, pp. 384–402.

    Google Scholar 

  • Millán, J. del R. (1995) Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot. Robotics and Autonomous Systems, 15(3), 275–300.

    Google Scholar 

  • Nuttin, M. and Van Brussel, H. (1995) Learning an industrial assembly task with complex objects and tolerances, in Fourth European Workshop on Learning Robots, Kaiser, M. (ed.), Karlsruhe, Germany.

  • Schneider, J. G. and Gans, R. F. (1994) Efficient search for robot skill learning: simulation and reality, in IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems (IROS '94), Munich, Germany, pp. 1256–1263.

  • Simsarian, K. T. and Mataric, M. J. (1995) Learning to cooperate using two six-legged mobile robots, in Proceedings of the 3rd European Workshop on Learning Robots (EWLR-3), Kaiser, M. (ed.), Heraklion, Crete, Greece.

    Google Scholar 

  • Smith, R. G. and Davis, R. (1981) Frameworks for cooperation in distributed problem solving. IEEE Transactions on Systems, Man, and Cybernetics 11(1), 61–70.

    Google Scholar 

  • Webster (n. d.) Webster's Encyclopedic Dictionary. Electronic ver-sion. Available at http://c.gp.cs.cmu.edu:5103/prog/webster.

  • Weiss, G. and Sen, S. (eds) (1995) Adaptation and Learning in Multi-Agent Systems, Springer-Verlag, Berlin, Heidelberg, New York.

    Google Scholar 

  • Zlotkin, G. and Rosenschein, J. S. (1989) Negotiation and task sharing among autonomous agents in cooperative domains, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 912–917.

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Friedrich, H., Rogalla, O. & Dillmann, R. Integrating skills into multi-agent systems. Journal of Intelligent Manufacturing 9, 119–127 (1998). https://doi.org/10.1023/A:1008811827890

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