ABSTRACT
Game playing has been a core domain of artificial intelligence research since the beginnings of the field. Game playing provides clearly defined arenas within which computational approaches can be readily compared to human expertise through head-to-head competition and other benchmarks. Game playing research has identified several simple core algorithms that provide successful foundations, with development focused on the challenges of defeating human experts in specific games. Key developments include minimax search in chess, machine learning from self-play in backgammon, and Monte Carlo tree search in Go. These approaches have generalized successfully to additional games. While computers have surpassed human expertise in a wide variety of games, open challenges remain and research focuses on identifying and developing new successful algorithmic foundations. WIREs Cogn Sci 2014, 5:193-205. doi: 10.1002/wcs.1278 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.
