The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
This addresses the problem of AI-assisted game design for game developers, offering an incremental improvement over prior evolutionary algorithms.
The paper tackled the problem of automatic game improvement by developing the N-Tuple Bandit Evolutionary Algorithm, which uses AI agent observations to estimate game quality and balances exploration and exploitation, resulting in more robust performance than existing methods like Random Mutation Hill Climber in optimizing a Space Battle game variant.
This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple algorithm.