As in some other computer applications, games raise the question of whether computers can make good decisions based on the evaluation of present and possible future situations. They also provide a suitable environment to support experimentation in different areas of computer science such as algorithms, data structures, machine learning, knowledge engineering, tree search, and reasoning.
If computers cannot solve decision-making problems in ``simple'' domains like games, then how can we be sure that they can make good decisions in other complex domains where rules are ill-defined, or there are high levels of uncertainty? Four characteristics make games suitable for computer representation:
Games are an abstraction of worlds in which hostile agents act to diminish each other's well-being. Thus, they can be used to design and analyze situations with multiple interacting agents having competing goals. Since real life contains many situations of this kind, a method to solve a game may be applied to problems in other areas. For example, in Theory of Games and Economic Behavior, Von Neumann and Morgenstern state that a study of ``games of strategy'' is required in order to develop a theory for the foundations of economics and for the main mechanisms of social organization, because games are analogous to a variety of behaviors and situations that occur in these two areas . In fact, games are already used to model certain economic problems.
In addition, the development of a program to play a strategic game often involves the application of theoretical concepts to practical situations. Programs that implement different theories can be played against each other to provide a comparison of the effectiveness of these theories in a practical domain. Therefore, games can be used as an experimental environment to obtain supporting or refuting evidence for new ideas, and to stimulate discussion on different approaches to solve a particular problem.