Abstract
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.
Paper submitted to the 5th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’98)
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References
C. Argyris and D.A. Schon: Organizational Learning, Addison-Wesley, 1978.
A.H. Bond and L.Gasser: Reading in Distributed Artificial Intelligence, Morgan Kaufmann Publishers, 1988.
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.
R. Espejo, W. Schuhmann, M. Schwaninger and U. Bilello: “Organizational Transformation and Learning”, John Wiley & Sons, 1996.
J.J. Grefenstette: “Credit Assignment in Rule Discovery Systems Base on Genetic Algorithms”, Machine Learning, Vol. 3, pp. 225–245, 1988.
D.E. Goldberg: “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, 1989.
M. Hirahara, N. Oka and K. Yoshida:, “Automatic placement using static and dynamic groupings”, Engineering Design & Automation, Vol. 3, No. 2, 167–178, 1997.
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.
D. Kim: “The Link between individual and organizational learning”, Sloan Management Review, Fall, pp. 37–50, 1993.
J.G. March. “Exploration and Exploitation in Organizational Learning”, Organizational Science, Vol. 2, No. 1, pp. 71–87, 1991.
K. Miyazaki, M. Yamamura and S. Kobayashi: “On the Rationality of Profit Sharing in Reinforcement Learning”, IIZUKA ’94, pp. 285–288, 1994.
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.
S.F. Smith: “Flexible learning of problem solving heuristics through adaptive search”, IJCAI ’83, pp. 422–425, 1983.
R.S. Sutton, A.G. Bart: Reinforcement Learning—An Introduction-, The MIT Press, 1998.
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.
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.
H. Yoshimura: “Knowledge-based placement and routing system for printed circuit board”, PRICAI ’90, pp. 116–121, 1990.
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.
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© 1998 Springer-Verlag Berlin Heidelberg
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Takadama, K., Nakasuka, S., Terano, T. (1998). Analyzing the roles of problem solving and learning in organizational-learning oriented classifier system. In: Lee, HY., Motoda, H. (eds) PRICAI’98: Topics in Artificial Intelligence. PRICAI 1998. Lecture Notes in Computer Science, vol 1531. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095259
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DOI: https://doi.org/10.1007/BFb0095259
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