Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games

TL;DR

This paper introduces an evolutionary algorithm that automatically generates two-player board games, guided by entertainment metrics, and validates the entertainment and learnability of the evolved games through user surveys and neural network testing.

Contribution

It presents a novel evolutionary approach with specific entertainment metrics for automatic game creation, validated by user surveys and neural network learnability tests.

Findings

Evolved games show comparable entertainment levels to popular existing games.

User surveys confirm the entertainment value of the generated games.

Neural network controllers demonstrate the learnability of the evolved games.

Abstract

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.