Analyzing the roles of problem solving and learning in organizational-learning oriented classifier system

  • Keiki Takadama
  • Shinichi Nakasuka
  • Takao Terano
Multi Agent Architecture
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1531)


This paper analyzes the roles of problem solving and learning in Organizational-learning oriented Classifier System (OCS) from the viewpoint of organizational learning in organization and management sciences, and validates the effectiveness of the roles through the experiments of large scale problem for Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). OCS is a novel multiagent-based architecture, and is composed of the following four mechanisms: (1) reinforcement learning, (2) rule generation, (3) rule exchange, and (4) organizational knowledge utilization. In this paper, we discuss that the four mechanisms in OCS work respectively as an individual performance/concept learning and an organizational performance/concept learning in organization and management sciences. Through the intensive experiments on the re-design problems of real scale PCBs, the results suggested that four learning mechanisms in individual/organizational levels contribute to finding not only feasible part placements in fewer iterations but also the shorter total wiring length than the one by human experts.


organizational learning learning classifier system multiagent system print circuit board design 


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  1. 1.
    C. Argyris and D.A. Schon: Organizational Learning, Addison-Wesley, 1978.Google Scholar
  2. 2.
    A.H. Bond and L.Gasser: Reading in Distributed Artificial Intelligence, Morgan Kaufmann Publishers, 1988.Google Scholar
  3. 3.
    R. Duncan and A. Weiss: “Organizational Learning: Implications for organizational design”, in Research in organizational behavior, B.M. Staw (Ed.), Vol. 1, JAI Press, pp. 75–123, 1979.Google Scholar
  4. 4.
    R. Espejo, W. Schuhmann, M. Schwaninger and U. Bilello: “Organizational Transformation and Learning”, John Wiley & Sons, 1996.Google Scholar
  5. 5.
    J.J. Grefenstette: “Credit Assignment in Rule Discovery Systems Base on Genetic Algorithms”, Machine Learning, Vol. 3, pp. 225–245, 1988.Google Scholar
  6. 6.
    D.E. Goldberg: “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, 1989.Google Scholar
  7. 7.
    M. Hirahara, N. Oka and K. Yoshida:, “Automatic placement using static and dynamic groupings”, Engineering Design & Automation, Vol. 3, No. 2, 167–178, 1997.Google Scholar
  8. 8.
    J.H. Holland and J. Reitman: “Cognitive Systems Based on Adaptive Algorithms”, in Pattern Directed Inference System, D.A. Waterman and F. Hayes-Roth (Eds.), Academic Press, 1978.Google Scholar
  9. 9.
    D. Kim: “The Link between individual and organizational learning”, Sloan Management Review, Fall, pp. 37–50, 1993.Google Scholar
  10. 10.
    J.G. March. “Exploration and Exploitation in Organizational Learning”, Organizational Science, Vol. 2, No. 1, pp. 71–87, 1991.MathSciNetCrossRefGoogle Scholar
  11. 11.
    K. Miyazaki, M. Yamamura and S. Kobayashi: “On the Rationality of Profit Sharing in Reinforcement Learning”, IIZUKA ’94, pp. 285–288, 1994.Google Scholar
  12. 12.
    C. Sechen and A. Sangiovanni-Vincentelli: “The Timber Wolf Placement and Routing package” IEEE Journal Solid-State Circuits, SC-20,2, pp. 510–522, 1985.CrossRefGoogle Scholar
  13. 13.
    S.F. Smith: “Flexible learning of problem solving heuristics through adaptive search”, IJCAI ’83, pp. 422–425, 1983.Google Scholar
  14. 14.
    R.S. Sutton, A.G. Bart: Reinforcement Learning—An Introduction-, The MIT Press, 1998.Google Scholar
  15. 15.
    K. Takadama, S. Nakasuka and T. Terano: “Printed Circuit Board Design via Organizational-Learning Agents”, Applied Intelligence: Special Issue on Intelligent Adaptive Agents, 1998, to appear.Google Scholar
  16. 16.
    K. Takadama, K. Hajiri, T. Nomura, M. Okada, S. Nakasuka and K. Shimohara: “Learning Model for Adaptive Behaviors as an Organized Group of Swarm Robots”, International Journal of Artificial Life and Robotics, 1998, to appear.Google Scholar
  17. 17.
    H. Yoshimura: “Knowledge-based placement and routing system for printed circuit board”, PRICAI ’90, pp. 116–121, 1990.Google Scholar
  18. 18.
    T. Yoshikawa, T. Furuhashi and Y. Uchikawa: “Coding Methods for Automatic Placement of Parts on Printed Circuit Boards” (in Japanese), Trans. of The Institute of Electrical Engineers of Japan, Vol. 115-D, No. 5, pp 642–651, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Keiki Takadama
    • 1
  • Shinichi Nakasuka
    • 1
  • Takao Terano
    • 2
  1. 1.The University of TokyoTokyoJapan
  2. 2.The University of TsukubaTokyoJapan

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