Iterative Query-Based Approach to Efficient Task Decomposition and Resource Allocation

  • Michal pěchouček
  • Ondřej Lerch
  • Jiří Bíba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4149)


Intelligent coordination in complex multi-agent environments requires sophisticated mechanisms for suboptimal task decomposition and efficient resource allocation provided by the the agents. Besides the quality of coordination (i.e. efficiency of decomposition and resource allocation) we need to handle also computational efficiency restriction such as fast response time and limited communication traffic among the agents as well as optimization of the amount of private knowledge disclosure, during collaboration patterns negotiation among the semi-collaborative agents. We present a novel contracting mechanism based on the use of the approximated acquaintance model, a structure where the agents store the information about the states, capabilities and resources of possible collaborators. We suggest an approach of iterative construction of the partially-linear acquaintance models that is beneficial mainly in complex agent communities.


Resource Allocation Multiagent System Decomposition Algorithm Query Message Construction Mechanism 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pĕchouček, M., Mařík, V., Bárta, J.: A knowledge-based approach to coalition formation. IEEE Intelligent Systems 17(3), 17–25 (2002)CrossRefGoogle Scholar
  2. 2.
    Pechoucek, M., Tozicka, J., Marik, V.: Meta-reasoning methods for agent‘s intention modelling. In: Autonomous Intelligent Systems: Agents and Data Mining, pp. 134–148. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Rehák, M., Pĕchouček, M., Tožička, J., Šišlák, D.: Using stand-in agents in partially accessible multi-agent environment. In: Gleizes, M.-P., Omicini, A., Zambonelli, F. (eds.) ESAW 2004. LNCS (LNAI), vol. 3451, pp. 277–291. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Sandholm, T.: Distributed Rational Decision Making. In: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 201–258. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    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
  6. 6.
    Sandholm, T., Lesser, V.: Coalitions among computationally bounded agents. Artificial Intelligence 94(1-2), 99–137 (1997)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Tambe, M.: Towards flexible teamwork. Journal of Artificial Intelligence Research 7, 83–124 (1997)Google Scholar
  8. 8.
    Sycara, K., Decker, K., Pannu, A., Williamson, M., Zeng, D.: Distributed intelligent agents. IEEE Expert 11(6), 36–46 (1996)CrossRefGoogle Scholar
  9. 9.
    Grosz, B., Kraus, S.: Collaborative plans for complex group action. Artificial Intelligence 86(2), 269–357 (1996)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Mařík, V., Pěchouček, M., Štěpánková, O.: Social Knowledge in Multi-agent Systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, p. 211. Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Pĕchouček, M., Mařík, V., Štĕpánková, 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
  12. 12.
    Cao, W., Bian, C.G., Hartvigsen, G.: Achieving efficient cooperation in a multi-agent system: The twin-base modeling. In: Kandzia, P., Klusch, M. (eds.) CIA 1997. LNCS (LNAI), vol. 1202, pp. 210–221. Springer, Heidelberg (1997)Google Scholar
  13. 13.
    Wittig, T.: ARCHON: An Architecture for Multi-agent System. Ellis Horwood, Chichester (1992)Google Scholar
  14. 14.
    Sanyal, S., Jain, A., Das, S., Biswas, R.: A hierarchical and distributed approach for mapping large applications to heterogeneous grids using genetic algorithms. In: Proceedings of the IEEE International Conference on Cluster Computing, pp. 496–499. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  15. 15.
    Gao, Y., Rong, H., Huang, J.Z.: Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems 21(1), 151–161 (2005)CrossRefGoogle Scholar
  16. 16.
    Fatima, S.S., Wooldridge, M.: Adaptive task and resource allocation in multi-agent systems. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 537–544. ACM, New York (2001)CrossRefGoogle Scholar
  17. 17.
    Li, C., Li, L.: Competitive proportional resource allocation policy for computational grid. Future Generation Computer Systems 20(6), 1041–1054 (2004)CrossRefGoogle Scholar
  18. 18.
    Lerch, O.: Effcient contraction mechanisms for virtual enterprises operation. Master’s thesis, Czech Technical University (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michal pěchouček
    • 1
  • Ondřej Lerch
    • 2
  • Jiří Bíba
    • 1
  1. 1.Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePragueThe Czech Republic
  2. 2.Department of Software Engineering, Faculty of Nuclear EngineeringCzech Technical University in PraguePragueThe Czech Republic

Personalised recommendations