An A-Team Based Architecture for Constraint Programming

  • Yujun Zheng
  • Lianlai Wang
  • Jinyun Xue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)


The paper proposes an agent-based constraint programming architecture that we have successfully applied to solve large, particularly combinatorial, operations problems. The architecture is based on the asynchronous team (A-Team) in which multiple problem solving agents cooperate with each other by exchanging results to produce a set of non-dominated solutions. We extend the A-Team by introducing CSP-specific agents, explicitly defining solution states, and enabling solution decomposition/composition, and thereby improve the performance, reliability, and automation of constraint programming significantly.


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  1. 1.
    Bistarelli, S.: Semirings for Soft Constraint Solving and Programming. LNCS, vol. 2962, pp. 1–20. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Alguire, K.M., Gomes, C.P.: Technology for Planning and Scheduling under Complex Constraints. In: Proceedings of Society for Optical Engineering, Boston, USA, pp. 101–107 (1997)Google Scholar
  3. 3.
    Beck, J.C., Fox, M.S.: Supply chain coordination via mediated constraint relaxation. In: Proceedings of 1st Canadian Workshop on Distributed Artificial Intelligence, Banff, AB, pp. 61–72 (1994)Google Scholar
  4. 4.
    Liu, J.S., Sycara, K.P.: Multiagent coordination in tightly coupled task scheduling. In: Proceedings of 2nd International Conference on Multiagent Systems, Kyoto, Japan, pp. 164–171 (1996)Google Scholar
  5. 5.
    Talukdar, S.N., Baerentzen, L., Gove, A., Souza, P.D.: Asynchronous Teams: Cooperation Schemes for Autonomous Agents. Journal of Heuristics 4(4), 295–321 (1998)CrossRefGoogle Scholar
  6. 6.
    Zheng, Y.J., Xue, J.Y.: MISCE: A Semi-Automatic Development Environment for Logistic Information Systems. In: Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics, Beijing, China, pp. 1020–1025 (2005)Google Scholar
  7. 7.
    Tsang, E.P.K.: Foundations of Constraint Satisfaction. Academic Press, London (1993)Google Scholar
  8. 8.
    Zheng, Y.J., Wang, L.L., Xue, J.Y.: A Constraint Programming Framework for Integrated Materiel Logistic Support. Journal of Nanjing University 40(10), 30–35 (2005)Google Scholar
  9. 9.
    Akkiraju, R., Keskinocak, P., Murthy, S., Frederick, W.: An Agent-Based Approach for Scheduling Multiple Machines. Applied Intelligence 14, 135–144 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Pepper, P., Smith, D.R.: A High-level Derivation of Global Search Algorithms (with Constraint Propagation). Science of Computer Programming. special issue on FMTA (Formal Methods: Theory and Applications), vol. 28, pp. 247–271 (1996)Google Scholar
  11. 11.
    Focacci, F., Lodi, A., Milano, M.: Optimization-Oriented Global Constraints. Constraints 7, 351–365 (2002)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Zweben, M., Daun, B., Davis, E., Deale, M.: Scheduling and rescheduling with iterative repair. In: Zweben, M., Fox, M.S. (eds.) Intelligent Scheduling, pp. 241–255. Morgan Kaufmann, San Francisco (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yujun Zheng
    • 1
    • 2
  • Lianlai Wang
    • 1
  • Jinyun Xue
    • 2
    • 3
  1. 1.Systems Engineering Institute of Engineer EquipmentBeijingChina
  2. 2.Institute of Software, Chinese Academy of SciencesBeijingChina
  3. 3.College of Computer Information and EngineeringJiangxi Normal UniversityNanchangChina

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