A study of organizational learning in multiagents systems

  • Masahiro Terabe
  • Takashi Washio
  • Osamu Katai
  • Tetsuo Sawaragi
Learning About/From Other Agents and the World

DOI: 10.1007/3-540-62934-3_48

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1221)
Cite this paper as:
Terabe M., Washio T., Katai O., Sawaragi T. (1997) A study of organizational learning in multiagents systems. In: Weiß G. (eds) Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1221. Springer, Berlin, Heidelberg

Abstract

In this paper, we are concerned with “organizational learning” in the multiagents systems. As an example for the organizational problem solving process, we will take the task allocation process. The process always enhances the performance of organization, however it is difficult for designers to make the process suitable for the organization and its environment. For that reason, the learning ability is necessary for the process, since it gives them adaptability and robustness. This paper is intend to investigate the relation between selection of task allocation style and its task allocation costs in the learning organization. We introduce an organizational learning model consisting of reinforcement learning agents. These agents learn about ability of other agents in the organization and themselves through their experience of interaction. Thus, we show the results of simulation, and discuss on them.

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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Masahiro Terabe
    • 1
  • Takashi Washio
    • 2
  • Osamu Katai
    • 3
  • Tetsuo Sawaragi
    • 3
  1. 1.Mitsubishi Research Institute, Inc.Japan
  2. 2.Institute of Scientific and Industrial ResearchOsaka UniversityJapan
  3. 3.Graduate School of EngineeringKyoto UniversityUSA

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