Concluding Remarks
In this chapter, we have described the basic idea of our business simulator development methodology from the conventional “human players only environment” to one with a mixture of both human and machine-learning agents. This enables us to enhance collaborative learning with business simulators.
From other experiments about much more complex cases, we conclude that the toolkit is effective for game designers to develop and tune their own simulators. We have conducted business modeling education over 5 years with more than 100 games. These games can be reimplemented using our learning agent architecture. Future work will include: (1) exploration of the “best solutions” of a certain class of games using the proposed architecture, and (2) employment of other learning techniques for software agents.
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Kobayashi, M., Terano, T. (2005). Exploring Business Gaming Strategies by Learning Agents. In: Arai, K., Deguchi, H., Matsui, H. (eds) Agent-Based Modeling Meets Gaming Simulation. Agent-Based Social Systems, vol 2. Springer, Tokyo. https://doi.org/10.1007/4-431-29427-9_6
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DOI: https://doi.org/10.1007/4-431-29427-9_6
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