Towards Simulation-Aided Design of Multi-Agent Systems

  • Michal Pěchouček
  • Michal Jakob
  • Peter Novák
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6599)

Abstract

With the growing complexity of multi-agent applications and environments in which they are deployed, there is a need for development techniques that would allow for early testing and validation of application design and implementation. This is particularly true in cases where the developed multi-agent application is to be closely integrated with an existing, real-world system of multi-agent nature.

Drawing upon our previous experiences with development of complex multi-agent applications, we propose simulation-aided design of multi-agent systems (SADMAS), a methodology tightly integrating simulations of the target system into the MAS application development process. In its heart lies the use of mixed-mode simulation, a simulation where parts of the deployed application operate in the target environment and parts remain simulated. We argue, that employing SADMAS process contributes to reduction of risks involved in development of complex MAS applications, as well as it helps to accelerate the process. Besides describing the capstones of the SADMAS approach and consequences of its application, we also illustrate it’s use on a case-study of a next-generation decentralised air traffic management system.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Babaoglu, O., Meling, H., Montresor, A.: Anthill: A framework for the development of agent-based peer-to-peer systems. In: International Conference on Distributed Computing Systems (ICDCS), pp. 15–22 (2002)Google Scholar
  2. 2.
    Beck, K.: Test Driven Development: By Example. Addison-Wesley Professional (November 2002)Google Scholar
  3. 3.
    Bernon, C., Gleizes, M.P., Peyruqueou, S., Picard, G.: ADELFE: A Methodology for Adaptive Multi-agent Systems Engineering. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS (LNAI), vol. 2577, pp. 156–169. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., Mylopoulos, J.: Tropos: An agent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems 8, 203–236 (2004)CrossRefMATHGoogle Scholar
  5. 5.
    Collier, N.: RePast: An extensible framework for agent simulation. Technical Report 36, The University of Chicago, Social Science Research (2003)Google Scholar
  6. 6.
    Duvall, P.M., Matyas, S., Glover, A.: Continuous Integration: Improving Software Quality and Reducing Risk. Addison-Wesley Professional (July 2007)Google Scholar
  7. 7.
    The European Organisation for the Safety of Air Navigation. EUROCONTROL BADA (2011), http://www.eurocontrol.int/eec/public/standard_page/proj_BADA.html
  8. 8.
    Horn, M.E.T.: Multi-modal and demand-responsive passenger transport systems: a modelling framework with embedded control systems. Transportation Research Part A: Policy and Practice 36(2), 167–188 (2002)Google Scholar
  9. 9.
    Jakob, M., Vaněk, O., Urban, Š., Benda, P., Pěchouček, M.: Employing Agents to Improve the Security of International Maritime Transport. In: Proceedings of the 6th workshop on Agents in Traffic and Transportation, ATT 2010 (May 2010)Google Scholar
  10. 10.
    Jakovljevic, G., Basch, D.: Implementing multiscale traffic simulators using agents. In: 26th International Conference on Information Technology Interfaces, vol. 1, pp. 519–524 (June 2004)Google Scholar
  11. 11.
    Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), vol. 3, pp. 2149–2154 (September 2004)Google Scholar
  12. 12.
    McCarthy, J.: Elaboration tolerance (1999), http://www-formal.stanford.edu/jmc/elaboration.html
  13. 13.
    Pavlíček, D., Jakob, M., Semsch, E., Pěchouček, M.: Occlusion-aware multi-uav surveillance of multiple urban areas. In: 6th Workshop on Agents in Traffic and Transportation, ATT 2010 (2010)Google Scholar
  14. 14.
    Padgham, L., Winikoff, M.: Prometheus: A practical agent-oriented methodology. Agent-Oriented Methodologies, 107–135 (2005)Google Scholar
  15. 15.
    Pěchouček, M., Šišlák, D., Pavlíček, D., Volf, P., Kopříva, Š.: AGENTFLY: Distributed Simulation of Air Traffic Control Using Unmanned Aerial Vehicles. In: Proceedings of 2nd Conference for Unmanned Aerial Systems, UAS (March 2010)Google Scholar
  16. 16.
    Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: Design and implementation. In: IEEE/PES Power Systems Conference and Exposition, PSCE 2009, pp. 1–8 (2009)Google Scholar
  17. 17.
    Šišlák, D., Volf, P., Pěchouček, M.: Agent-Based Cooperative Decentralized Airplane-Collision Avoidance. IEEE Transactions on Intelligent Transportation Systems (99), 1–11 (2009)Google Scholar
  18. 18.
    Šišlák, D., Volf, P., Pěchouček, M.: Agent-Based Cooperative Decentralized Airplane-Collision Avoidance. IEEE Transactions on Intelligent Transportation Systems (99), 1–11 (2010)Google Scholar
  19. 19.
    Procerus Technologies. Procerus Technologies: Fly Light with world’s smallest UAV Autopilot (2011), http://procerusuav.com/
  20. 20.
    Wilensky, U.: Netlogo. Technical report, Center for Connected Learning and Computer-Based Modeling, Northwestern University (1999), http://ccl.northwestern.edu/netlogo/
  21. 21.
    Zambonelli, F., Jennings, N.R., Wooldridge, M.: Developing multiagent systems: The gaia methodology. ACM Trans. Softw. Eng. Methodol. 12(3), 317–370 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michal Pěchouček
    • 1
  • Michal Jakob
    • 1
  • Peter Novák
    • 1
  1. 1.Agent Technology Center, Dept. of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PragueCzech Republic

Personalised recommendations