pRoPhEt MAS: Reactive Planning Engine for Multiagent Systems

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Autonomous mobile robots can substantially increase their effectiveness in dynamic environments using planning. This paper proposes a design for a soft real-time planning system for autonomous robots and offers a generic and modular approach to control a team of robots. Our system pRoPhEt MAS is based on ALICA (A Language for Interactive Cooperative Agents) and offers the coordination of team behaviors at runtime. In the evaluation scenario the system pRoPhEt MAS uses a state of the art planner “Fast Downward Planning System.” The evaluation focuses on planning during execution time. The team executes the best solutions found, selected by the heuristic, under certain time constraints. The results show that the execution with soft real-time planning is as good as sequential planning and execution. Hence, it offers the ability to react quickly in dynamic domains.

Keywords

Multiagent systems Dynamic domains Planning 

Notes

Acknowledgments

The project IMPERA is funded by the German Space Agency (DLR, Grant number: 50RA1112) with federal funds of the Federal Ministry of Economics and Technology (BMWi) in accordance with the parliamentary resolution of the German Parliament.

References

  1. 1.
    Brenner, M., Nebel, B.: Continual planning and acting in dynamic multiagent environments. Autonomous Agents and Multi-Agent Systems, 19(3):297–331 (2009)Google Scholar
  2. 2.
    Brunskill, Emma and Russell, Stuart J.: RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains. CoRR (2012)Google Scholar
  3. 3.
    Ghallab, M., Isi, C. K., Penberthy, S. Smith, D. E., Sun, Y., and Weld, D.: PDDL - The Planning Domain Definition Language. Technical report, CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control (1998)Google Scholar
  4. 4.
    Helmert, M.:The Fast Downward Planning System. Journal of Artificial Intelligence Research 26 (2006)Google Scholar
  5. 5.
    Kumar, A., Zilberstein, S., and Toussaint, M.: Scalable Multiagent Planning Using Probabilistic InferenceGoogle Scholar
  6. 6.
    McCoy, J. and Mateas, M. An integrated agent for playing real-time strategy games. In Proceedings of the 23rd national conference on Artificial intelligence - Volume 3, AAAI’08, pages 1313–1318. AAAI Press (2008)Google Scholar
  7. 7.
    Russell, S. J. and Zilberstein, S.: Anytime sensing, planning, and action: A practical model for robot control, pages 1402–1407. Proceedings of the International Conference on Artificial Intelligence (1993)Google Scholar
  8. 8.
    Skubch, H., Wagner, M., Reichle, R., and Geihs, K.: A modelling language for cooperative plans in highly dynamic domains. Mechatronics, 21:423–433 (2011)Google Scholar
  9. 9.
    Skubch, H., Wagner, M., Reichle, R., Triller, S., and Geihs, K.: Towards a comprehensive teamwork model for highly dynamic domains. In Filipe, J., Fred, A., and Sharp, B., editors, Proceedings of the 2nd International Conference on Agents and Artificial Intelligence, volume 2, page 121–127. INSTICC Press, INSTICC Press (2010)Google Scholar
  10. 10.
    Skubch, H.:Modelling and Controlling of Behaviour for Autonomous Mobile Robots. Westdeutscher Verlag GmbH (2013)Google Scholar
  11. 11.
    Ulusar, U. D.: Design and implementation of a real time planning system for autonomous robots. volume 1, page 74–79. Industrial Electronics, IEEE International (2006)Google Scholar
  12. 12.
    Zickler, S., Laue, T., Birbach, O., Wongphati, M., and Veloso, M. M. Ssl-vision: The shared vision system for the robocup small size league. In Baltes, J., Lagoudakis, M. G., Naruse, T., and Ghidary, S. S., editors, RoboCup, volume 5949 of Lecture Notes in Computer Science, pages 425–436. Springer (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Distributed Systems GroupUniversity of KasselKasselGermany

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