Measuring entertainment and automatic generation of entertaining games

With the advancement in technology and decrease in prices of electronic items, Personal Computers (PCs) are becoming common. This has resulted in PCs replacing many other electronic gadgets (televisions, play s tations, etc.), as people are inclined to use televisions and play stations in their PCs through software. Games are not an exception from this electronic advancement. All 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 PCs. 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. 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 order to address afore mentioned challenges (measuring of entertainment and automated generation of games). First task is to devise some metrics that can quantify the entertainment value of a game. Although creating a single quantitative measure for all genres of games is not trivial, but a separate metrics for each can be devised. Based upon the entertainment metrics some computational intelligence techniques can be used to create new and entertaining games. In this work we create a set of metrics for measuring entertainment in computer games. The genres we address are board based games and video games (predator/prey and platform games). The metrics are based theories of entertainment in 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.