Advertisement

Towards Time Management Adaptability in Multi-agent Systems

  • Alexander Helleboogh
  • Tom Holvoet
  • Danny Weyns
  • Yolande Berbers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3394)

Abstract

So far, the main focus of research on adaptability in multi-agent systems (MASs) has been on the agents’ behavior, for example on developing new learning techniques and more flexible action selection mechanisms. In this paper, we introduce a different type of adaptability in MASs, called time management adaptability. Time management adaptability focuses on adaptability in MASs with respect to execution control. First, time management adaptability allows a MAS to be adaptive with respect to its execution platform, anticipating arbitrary and varying timing delays which can violate correctness. Second, time management adaptability allows the execution policy of a MAS to be customized at will to suit the needs of a particular application. We discuss the essential parts of time management adaptability: (1) we employ time models as a means to explicitly capture the execution policy derived from the application’s execution requirements, (2) we classify and evaluate time management mechanisms which can be used to enforce time models, and (3) we introduce a MAS execution control platform which combines both previous parts to offer high-level execution control.

Keywords

Time Management Time Model Multiagent System Cognitive Agent Logical Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weyns, D., Holvoet, T.: The packet-world as a case to study sociality in multi-agent systems. In: Autonomous Agents and Multi-Agent Systems, AAMAS 2002 (2002)Google Scholar
  2. 2.
    Sauter, J.A., Matthews, R., Dyke, H.V.: Evolving adaptive pheromone path planning mechanisms. Autonomous Agents and Multiagent Systems, AAMAS 2002 (2002)Google Scholar
  3. 3.
    Parunak, H.V.D., Brueckner, S.: Ant-like missionaries and cannibals: Synthetic pheromones for distributed motion control. In: Sierra, C., Gini, M., Rosenschein, J.S. (eds.) Proceedings of the Fourth International Conference on Autonomous Agents, Barcelona, Catalonia, Spain, pp. 467–474. ACM Press, New York (2000)CrossRefGoogle Scholar
  4. 4.
    Brueckner, S.A.: Return From The Ant – Synthetic Ecosystems For Manufacturing Control. PhD thesis, Humboldt University Berlin, Department of Computer Science (2000)Google Scholar
  5. 5.
    Steels, L.: Cooperation between distributed agents through self-organization. Decentralized A.I (1990)Google Scholar
  6. 6.
    Axtell, R.: Effects of interaction topology and activation regime in several multi-agent systems. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS, vol. 1979, pp. 33–48. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Page, S.: On incentives and updating in agent based models. Journal of Computational Economics 10, 67–87 (1997)MATHCrossRefGoogle Scholar
  8. 8.
    Cornforth, D., Green, D.G., Newth, D., Kirley, M.: Do artificial ants march in step? ordered asynchronous processes and modularity in biological systems. In: Proceedings of the Eighth International Conference on Artificial Life, pp. 28–32. MIT Press, Cambridge (2003)Google Scholar
  9. 9.
    Fujimoto, R.: Time management in the high level architecture. Simulation, Special Issue on High Level Architecture 71, 388–400 (1998)Google Scholar
  10. 10.
    Wooldridge, M.J.: Multi-agent systems: an introduction. Wiley, Chichester (2001)Google Scholar
  11. 11.
    Parunak, H.V.D., Brueckner, S., Sauter, J., Matthews, R.S.: Distinguishing environmental and agent dynamics: A case study in abstraction and alternate modeling technologies. In: Omicini, A., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2000. LNCS (LNAI), vol. 1972, p. 19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 21, 558–565 (1978)MATHCrossRefGoogle Scholar
  13. 13.
    Misra, J.: Distributed discrete-event simulation. Computing Surveys 18, 39–65 (1986)CrossRefGoogle Scholar
  14. 14.
    Anderson, S.D., Cohen, P.R.: Timed common lisp: the duration of deliberation. SIGART Bull 7, 11–15 (1996)CrossRefGoogle Scholar
  15. 15.
    Uhrmacher, A., Kullick, B.: Plug and test software agents in virtual environments. In: Winter Simulation Conference, WSC 2000 (2000)Google Scholar
  16. 16.
    Anderson, S.D.: Simulation of multiple time-pressured agents. In: Winter Simulation Conference, WSC 1997, pp. 397–404 (1997)Google Scholar
  17. 17.
    Anderson, S.: A Simulation Substrate for Real-Time Planning. PhD thesis, University of Massachusetts at Amherst (1995)Google Scholar
  18. 18.
    Weyns, D., Holvoet, T.: Formal model for situated multi-agent systems. Formal Approaches for Multi-agent Systems, Special Issue of Fundamenta Informaticae (2004)Google Scholar
  19. 19.
    Ferber, J., Muller, J.: Influences and reaction: a model for situated multiagent systems. In: Proceedings of the 2th International Conference on Multi-Agent Systems. AAAI Press, Menlo Park (1996)Google Scholar
  20. 20.
    Weyns, D., Holvoet, T.: Regional synchronization for simultaneous actions in situated multiagent systems. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 497–511. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Chandy, K.M., Misra, J.: Asynchronous distributed simulation via a sequence of parallel computations. Communications of the ACM 24, 198–205 (1981)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Uhrmacher, A., Gugler, K.: Distributed, parallel simulation of multiple, deliberative agents. In: Proceedings of the 14th Workshop on Parallel and Distributed Simulation, PADS 2000, Bologna, Italy, pp. 101–110. IEEE, Los Alamitos (2000)CrossRefGoogle Scholar
  23. 23.
    Jefferson, D., Sowizral, H.: Fast concurrent simulation using the time warp mechanism. In: Proceedings of the SCS Multiconference on Distributed Simulation, pp. 63–69 (1985)Google Scholar
  24. 24.
    Helleboogh, A., Holvoet, T., Weyns, D.: Time management support for simulating multi-agent systems. In: Joint Workshop on Multi-Agent and Multi-Agent Based Simulation, MAMABS (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alexander Helleboogh
    • 1
  • Tom Holvoet
    • 1
  • Danny Weyns
    • 1
  • Yolande Berbers
    • 1
  1. 1.AgentWise, DistriNet, Department of Computer ScienceK.U.LeuvenBelgium

Personalised recommendations