The Most Promising Sequence of Moves using Monte-Carlo Tree Search on a Small Go Board

The game of Go was invented at least 2,500 years ago in China and aims to surround more territory than the opponent to win a game. Despite its simple rule, Go is a very complex strategy board game in the field of Artificial Intelligence (AI). The Monte-Carlo Tree Search (MCTS) is an algorithm with best-first search to overcome the difficulty of state evaluation and to reduce the enormous branching factor in a game tree. In the game of computer Go it performs numerous playouts to approximately estimate the win rates of the next candidate moves by sampling a sequence of moves starting the current node to the terminal node in a game tree. All powerful computer Go programs including AlphaGo have used MCTS to find the most promising move in playing computer Go game. We tried to find the most promising sequence of moves using pure MCTS in playing a computer game of small board Go. The experimental result shows that pure MCTS on a small Go board proceeds only two phases, the middle and the end games, rather than three phases, the opening, the middle and the end games happening in a 19×19 Go game; It also lacks of understanding of lower-level knowledge such as tesuji and oki.