Evolutionary Algorithms towards Generating Entertaining Games

Computer games are gaining popularity by every passing day. This has increased the number of choices in computer games for the users. At the same time the quality of entertainment provided by these games has also decreased due to abundance of games in the market for personal computers. On the other hand the task of game development for the developers is becoming tiresome, which requires scripting the game, modeling its contents and other such activities. Still it cannot be known how much the developed game is entertaining for the end users. As entertainment is a subjective term. What might be entertaining for one user may not be entertaining for others. Another issue from the point of view of game developers is the constant need of writing new games, requiring investment both in terms of time and resources. In this work we create a set of metrics for measuring entertainment in computer games. The genres we address are board based games and predator/prey type of games. The metrics devised are based on different theories of entertainment specifically related to computer games, taken from literature. Further we use Evolutionary Algorithm (EA) to generate new and entertaining games using the proposed entertainment metrics as the fitness function. The EA starts with a randomly initialized set of population and using genetic operators (guided by the proposed entertainment metrics) we reach a final set of population that is optimized against entertainment. For the purpose of verifying the entertainment value of the evolved games with that of the human we conduct a human user survey and experiment using the controller learning ability.