A Survey of Learning Classifier Systems in Games

作者：Shafi Kamran^*; Abbass Hussein A

来源：IEEE Computational Intelligence Magazine, 2017, 12(1): 42-55.

DOI：10.1109/MCI.2016.2627670

ames are becoming increasingly indispensable, not only for fun but also to support tasks that are more serious, such as education, strategic planning, and understanding of complex phenomena. Computational intelligence- based methods are contributing significantly to this development. Learning Classifier Systems (LCS) is a pioneering computational intelligence approach that combines machine learning methods with evolutionary computation, to learn problem solutions in the form of interpretable rules. These systems offer several advantages for game applications, including a powerful and flexible agent architecture built on a knowledgebased symbolic modeling engine; modeling flexibility that allows integrating domain knowledge and different machine learning mechanisms under a single computational framework; an ability to adapt to diverse game requirements; and an ability to learn and generate creative agent behaviors in real-time dynamic environments. We present a comprehensive and dedicated survey of LCS in computer games. The survey highlights the versatility and advantages of these systems by reviewing their application in a variety of games. The survey is organized according to a general game classification and provides an opportunity to bring this important research direction into the public eye. We discuss the strengths and weaknesses of the existing approaches and provide insights into important future research directions.

• 出版日期2017-2