Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games
This work addresses the need for automated game creation to reduce developer effort, but it is incremental as it builds on existing evolutionary methods with new metrics.
The authors tackled the problem of automatically generating entertaining two-player board games using an evolutionary strategy guided by proposed entertainment metrics, and they validated the entertainment value of evolved games through user surveys and neural network-based learnability tests.
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.