Gaming is regarded by many people as a new and promising tool to deal with complex problems in which human decisions have far-reaching effects on others. It needs to involve a number of key persons interacting in a simulation of a real-world problem for various purposes such as decision making, education, training, etc. However, generally speaking, key persons are usually busy dealing with their important jobs in their offices. This means that we cannot carry out game playing when some of the key persons are unable to attend on. Therefore, we often encounter the following problem: “Is there any appropriate method by which game playing can be successfully done even when some of the players are unable to attend on?”
In the first half of this paper, we suggest that an artificial neural network can be utilized to act for a player who is unable to attend on the game playing. That is to say, we suggest that an artificial neural network can mimic a strategy of a player by training its weight vector. In order to confirm this idea, an artificial neural network is applied to the computer gaming system of the COMMONS GAME which is one of the most famous environmental games in the world.
In the latter half of this paper, we suggest that genetic algorithms can be utilized in order to let game playings become much more exciting. As the first example, genetic algorithms are utilized to find an appropriate point of each card at the COMMONS GAME. Further, as the second example, genetic algorithms are utilized at the computer simulation game in order to let computer side have enough intelligence to cope with a human player.

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