PRACTICAL AND THEORETICAL ISSUES OF EVOLVING BEHAVIOUR TREES FOR A TURN-BASED GAME

The concept of evolving components of an artificial intelligence (AI) has seen increased interest in recent years as the power and complexity of AI has grown. In entertainment software, this AI can impact the player’s experiences and enjoyment through elements such as the level of difficulty of the player’s competition. There-fore AI development is an important research topic, especially as development is considered difficult by the video game industry. This work applies the evolutionary computing paradigm to a turn-based domain by evolving team strategies. These strategies are represented as behaviour trees, a formalism found in the video game industry and well-suited to the evolutionary algorithm due to their flexibility and tree structure. During the evolutionary process, strategies are evaluated in Battle for Wesnoth, an open-source game with a stochastic nature. A fitness function is defined to assign strategies a numerical strength value, along with a second perfor-mance metric that is robust to the variance found in the domain. The evolutionary algorithm then recombines strategies with high strength values, using evolutionar