Premium
Game playing
Author(s) -
Rosin Christopher D.
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.1278
Subject(s) - monte carlo tree search , computer science , variety (cybernetics) , competition (biology) , core (optical fiber) , key (lock) , domain (mathematical analysis) , minimax , field (mathematics) , game theory , data science , combinatorial game theory , artificial intelligence , human–computer interaction , sequential game , monte carlo method , computer security , mathematical economics , mathematics , ecology , telecommunications , mathematical analysis , statistics , pure mathematics , biology
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 This article is categorized under: Computer Science > Artificial Intelligence