By Hiroshi Deguchi (auth.), Kiyoshi Arai D.Eng., Hiroshi Deguchi D.Sc, D.Econ., Hiroyuki Matsui D.Eng. (eds.)
This selection of very good papers cultivates a brand new viewpoint on agent-based social method sciences, gaming simulation, and their hybridization. lots of the papers incorporated right here have been awarded within the exact consultation titled Agent-Based Modeling Meets Gaming Simulation at ISAGA2003, the thirty fourth annual convention of the overseas Simulation and Gaming organization (ISAGA) at Kazusa Akademia Park in Kisarazu, Chiba, Japan, August 25–29, 2003. This post-proceedings was once supported by means of the twenty-?rst century COE (Centers of Excellence) software production of Agent-Based Social structures Sciences (ABSSS), demonstrated on the Tokyo Institute of know-how in 2004. the current quantity contains papers submitted to the detailed consultation of ISAGA2003 and offers an outstanding instance of the varied scope and conventional of analysis accomplished in simulation and gaming this present day. The topic of the distinctive consultation at ISAGA2003 was once Agent-Based Modeling Meets Gaming Simulation. these days, agent-based simulation is turning into extremely popular for modeling and fixing advanced social phenomena. it's also used to reach at sensible suggestions to social difficulties. whilst, despite the fact that, the validity of simulation doesn't exist within the magni?cence of the version. R. Axelrod stresses the simplicity of the agent-based simulation version during the “Keep it basic, silly” (KISS) precept: As a fantastic, easy modeling is essential.
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Additional resources for Agent-Based Modeling Meets Gaming Simulation
Decision making items at each round: raw material procurement, production instructions, and sales price; 5. Game setting: Initial raw material stock: 400, transportation and production delay: one term, transportation and stock charges: one money unit per item. The lower the price, the larger the demand. Demand forecast after two rounds is shown to a player. Number of software agents: six agents with Exploring Business Gaming Strategies by Learning Agents 53 a. Reactive strategy (three agents), b.
This makes human players advantageous. In this case, (Fig. 2) the ﬁrst and last players are both humans, and the second and second last players are software agents. The software agents do not fre- Exploring Business Gaming Strategies by Learning Agents 55 Fig. 2. Cash deposit of each player quently lose, although they did not outperform human players that made no mistakes. We would like to have such a game balance to develop the game. It is a critical point to let the game have a sophisticated balance for play.
Kobayashi and T. Terano Table 2. XCS Parameter setting for the experiments Parameters in XCS Max population numbers Learning ratio Fitness parameters on CF Accuracy Discount ratio of pred. Threshold of GA Mutation ratio Threshold of deletion Ratio of average CF and CF Threshold of inc. 5 CF, classiﬁer; GA, genetic algorithm Table 3. Performance of reactive agents and human players Player Reward Reactive agent Human players 133 (126) 174 (141) Values in parentheses are rounded to the resolutions shown in Table 1 Experimental Results As the baseline of the series of the experiments described below, we validated the game rewards of the reactive agents and human (best) players in Table 3.
Agent-Based Modeling Meets Gaming Simulation by Hiroshi Deguchi (auth.), Kiyoshi Arai D.Eng., Hiroshi Deguchi D.Sc, D.Econ., Hiroyuki Matsui D.Eng. (eds.)