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Agent-based simulation to analyze business office activities using reinforcement learning

  • Yukinao Kenjo
  • Takashi Yamada
  • Takao Terano
Part of the Springer Series on Agent Based Social Systems book series (ABSS, volume 6)

Abstract

This paper attempts to clarify team behavior in cooperative organizations by agent-based simulations. We focus attention on both the roles of manages and the initiatives of staffs, and then model them using agent-based model concepts. This enables us to investigate phenomena in organizations at micro-level and macro-level. Besides, we formulate the task processing of each member in real organizations as learning for maze problem. The advantages of applying maze problem for our simulation model are as follows: It is possible to describe agents who acquire skills by reinforcement learning and to represent environmental uncertainty by changing block placements dynamically. Several computational experiments show what the whole organization behaves from microscopic points of view. At the same time, the authors confirm that the ability to adapt environments under uncertainty is different from the characters of organization.

Keywords

Reinforcement Learning Task Processing Middle Management Environmental Uncertainty Work Content 
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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Copyright information

© Springer 2009

Authors and Affiliations

  • Yukinao Kenjo
    • 1
  • Takashi Yamada
    • 1
  • Takao Terano
    • 1
  1. 1.Department of Computational Intelligence and System ScienceTokyo Institute of Technology (Now at Sony Corporation)Yokohama, KanagawaJapan

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