Advertisement

Multilevel Approach to Agent-Based Task Allocation in Transportation

  • Martin Rehák
  • Přemysl Volf
  • Michal Pěchouček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4149)

Abstract

We present a hybrid algorithm for distributed task allocation problem in a cooperative logistics domain. Our approach aims to achieve superior computational performance by combining the classic negotiation techniques and acquaintance models from agent technology field with methods from the operation research and AI planning. The algorithm is multi-stage and makes a clear separation between discreet planning that defines the tasks and allocation of resources to available tasks. Task allocation starts with centralized planning based on acquaintance model information that prepares a framework for efficient distributed negotiation. The subsequent distributed part of the task allocation process is parallel for all tasks and allows the agents to optimally allocate their resources to proposed tasks and to further optimize the allocation by negotiation with other agents. Parallel execution of the task allocation mechanism allows the algorithm to answer the planning request in predictable time, albeit at expense of possible non-optimality. In the experiments, we evaluate the relative importance of OR and negotiation parts of the task allocation process.

Keywords

Social Knowledge Multiagent System Task Allocation Initial Planning Multilevel Approach 
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.
    Fischer, K., Muller, J.P., Pischel, M., Schier, D.: A model for cooperative transportation scheduling. In: Proceedings of the First International Conference on Multiagent Systems, Menlo park, California, pp. 109–116. AAAI Press / MIT Press (1995)Google Scholar
  2. 2.
    Perugini, D., Lambert, D., Sterling, L., Pearce, A.: A distributed agent approach to global transportation scheduling. In: The 2003 IEEE/WIC International Conference on Intelligent Agent Technology (IAT 2003), Halifax, Canada, pp. 18–24 (2003)Google Scholar
  3. 3.
    Smith, R.G.: The contract net protocol: High level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  4. 4.
    Pĕchouc̆ek, M., Mařík, V., Bárta, J.: A knowledge-based approach to coalition formation. IEEE Intelligent Systems 17(3), 17–25 (2002)Google Scholar
  5. 5.
    Sandholm, T.: Contract types for satisficing task allocation: I theoretical results. In: Proceedings of the AAAI Spring Symposium (1998)Google Scholar
  6. 6.
    Perugini, D., Lambert, D., Sterling, L., Pearce, A.: Agent-based global transportation scheduling in military logistics. In: AAMAS 2004: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1278–1279. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  7. 7.
    Carlsson, C., Fullér, R.: Fuzzy Reasoning in Decision Making and Optimization. Physica Verlag, Springer, Heidelberg (2002)MATHGoogle Scholar
  8. 8.
    Pechoucek, M., Mařík, V., Štěpánková, O.: Role of acquaintance models in agent-based production planning systems. In: Klusch, M., Kerschberg, L. (eds.) CIA 2000. LNCS (LNAI), vol. 1860, pp. 179–190. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Witteveen, C., Roos, N., van der Krogt, R., de Weerdt, M.: Diagnosis of single and multi-agent plans. In: AAMAS 2005: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pp. 805–812. ACM Press, New York (2005)Google Scholar
  10. 10.
    Miguel, I., Jarvis, P., Shen, Q.: Flexible graphplan. In: Horn, W. (ed.) Proceedings of the Fourteenth European Conference on Artificial Intelligence, pp. 506–510 (2000)Google Scholar
  11. 11.
    S̆is̆lák, D., Rehák, M., Pĕchouc̆ek, M., Rollo, M., Pavlíček, D.: A-globe: Agent development platform with inaccessibility and mobility support. In: Unland, R., Klusch, M., Calisti, M. (eds.) Software Agent-Based Applications, Platforms and Development Kits, pp. 21–46. Birkhäuser Verlag, Basel (2005)Google Scholar
  12. 12.
    Lawler, E.L., Wood, D.E.: Branch-and-bound methods: A survey. Operations Research 14(4), 699–719 (1966)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Rehák
    • 1
  • Přemysl Volf
    • 1
  • Michal Pěchouček
    • 1
  1. 1.Department of Cybernetics and Center for Applied CyberneticsCzech Technical UniversityPragueCzech Republic

Personalised recommendations