Title :
Current Frontiers in Computer Go
Author :
Rimmel, Arpad ; Teytaud, Olivier ; Lee, Chang-Shing ; Yen, Shi-Jim ; Wang, Mei-Hui ; Tsai, Shang-Rong
Author_Institution :
LRI, Univ. Paris-Sud, Orsay, France
Abstract :
This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for the game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the Computer Go event organized at the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2009), in which four main Go programs played against top level humans. We see that in 9 × 9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19 × 19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9 × 9 Go occurred in this event.
Keywords :
Monte Carlo methods; computer games; fuzzy systems; game theory; tree searching; FUZZ IEEE 2009; IEEE international conference; Monte Carlo tree search; computer go; fuzzy system; Algorithm design and analysis; Decision trees; Game theory; Games; Monte Carlo methods; Game of Go; Monte Carlo tree search (MCTS); upper confidence;
Journal_Title :
Computational Intelligence and AI in Games, IEEE Transactions on
DOI :
10.1109/TCIAIG.2010.2098876