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Autonomous-Centered Problem Allocation Oriented to Cooperation

  • Xiping Liu
  • Wanchun Dou
  • Guihai Chen
  • Shijie Cai
  • Jiashan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3756)

Abstract

By reasonably allocating a cooperative problem which need multiple solvers cope with together, the problem could be performed more effectively and efficiently. A problem could be divided into multiple sub-problems; each has certain ability requirement which is the hinge to relate problem and solver. According to ability requirement, the solver candidate set for each sub-problem could be established. To select suitable solver from candidate set so as to solve a cooperative problem in more autonomous and consistent way, a mathematical allocation model taking the minimization of interaction number as objective function is established. The model solving process is deployed by decreasing two kinds of interactions, i.e. intra-interaction and extra-interaction. Experiment shows this method obtains better performance than general allocation.

Keywords

Problem Model Cooperative System Allocation Process Selection Principle Suitable Solver 
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.

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References

  1. 1.
    Rogers, E.: Cognitive cooperation through visual interaction. Int. J. Knowledge-Based Systems, 117–125 (1995)Google Scholar
  2. 2.
    Pérez, J.A., Corchuelo, R., Ruiz, D., Toro, M.: An Order-Based, Distributed Algorithm for Implementing Multiparty Interactions. In: Arbab, F., Talcott, C. (eds.) COORDINATION 2002. LNCS, vol. 2315, pp. 250–257. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Ruiz, D., Corchuelo, R., Pérez, J.A., Toro, M.: An Algorithm for Ensuring Fairness and Liveness in Non-deterministic Systems Based on Multiparty Interactions. In: Monien, B., Feldmann, R.L. (eds.) Euro-Par 2002. LNCS, vol. 2400, pp. 563–572. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Sanchis, L.E.: Set theory, an operational approach. Gordon and Breach, Amsterdam (1996)zbMATHGoogle Scholar
  5. 5.
    Sova, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole (2000)Google Scholar
  6. 6.
    Minyi, Y.: Introduction to Combinatorial Optimization. Zhejiang Science & Technology Publishing House (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiping Liu
    • 1
    • 2
  • Wanchun Dou
    • 1
    • 2
  • Guihai Chen
    • 1
    • 2
  • Shijie Cai
    • 1
    • 2
  • Jiashan Tang
    • 3
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing University  
  2. 2.Dept. of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.Dept. of Applied Mathematics and PhysicsNanjing University of Posts and TelecommunicationsNanjingChina

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