This research reports an application of the agent approach to business games. We will discuss the topic of the analyzing business decision making procedure by using Human- and Software-Agent integration in business game domains. The agent approach is attracting attention in social sciences from the viewpoint of cooperation of both software and human players and agents. A business game is a kind of the gaming simulation which uncovers the decision making procedures executed by the plural participant players who have a common purpose is to solve a specific problem [Duke 1974]. In order to achieve the purpose, we have developed a game construction toolkit, which consists of a simple Business Model Description Language (BMDL), Agent Rules written in Ruby language [Matsumoto 1996], and their Business Model Development System (BMDS) [Fujimori 1999] [Terano 1999]. Furthermore, we have linked the software agents with machine learning programs by inter process communication. The developed simulators can be used by both human users and software agents in the WWW environment. Through the educational experience and intensive computer experiments, we have found the decision making procedures to a specific business model. This research describes the background and motivation, basic principles, the architecture and implementation of BMDL/Agent Rules/BMDS, some results of experiments as current detail, and learning software agent as a new function. The main contribution of the research is 1) to propose a general architecture for the human- and software-agent integration approach to analyzing of decision making procedures for an arbitrary business model by learning software agents, and 2) to demonstrate the effectiveness of human players and learning software agents to a business simulator by exemplifying the novel business simulation toolkit. This paper is organized as follows: 1. To discuss the background and motivation of the research. 2. To explain the basic architecture. 3. To explain the implementation of the agent system. 4. To explain the implementation of learning system [Butz 2000] [Takadama 2000]. 5. To describe the experimental setup and the results. 6. To give some concluding remarks and future work. In conclusion, by using learning software agent and human players, we have shown the effectiveness to analyze the decision making principles in a human complex system. The future work includes to uncover how mutual learning by plural human and software agents grows up global beneficial systems in business environment.
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