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코호넨 신경망을 이용 바둑 사활문제를 풀기 위한 후보 첫 수들
이병두,금영욱 한국게임학회 2009 한국게임학회 논문지 Vol.9 No.1
In the game of Go, the life-and-death problem is a fundamental problem to be definitely overcome when implementing a computer Go program. To solve local Go problems such as life-and-death problems, an important consideration is how to tackle the game tree's huge branching factor and its depth. The basic idea of the experiment conducted in this article is that we modelled the human behavior to get the recognized first moves to kill the surrounded group. In the game of Go, similar life-and-death problems(patterns) often have similar solutions. To categorize similar patterns, we implemented Kohonen Neural Network(KNN) based clustering and found that the experimental result is promising and thus can compete with a pattern matching method, that uses supervised learning with a neural network, for solving life-and-death problems. 바둑에 있어 사활문제는 컴퓨터 바둑을 구현하기 위해 반드시 극복해야 하는 기본적인 문제이다. 사활문제와 같은 국부적인 바둑 문제를 해결하기 위하여 고려해야 될 중요한 사항은 게임 트리의 엄청난 분기수와 그 깊이를 어떻게 처리하느냐이다. 본 논문에서 수행된 실험의 기본 착상은 둘러싸인 돌들을 죽이기 위해 인식된 첫 수들을 찾아내는 인간의 습성을 모방한 것이다. 바둑에 있어, 유사한 사활문제(패턴)들은 자주 유사한 해들을 갖는다. 유사한 패턴을 분류 하기 위하여 코호넨 신경망(KNN)을 기반으로 한 군집화를 수행하였으며, 실험 결과는 고무적이며 사활문제를 풀기 위해 신경망으로 통제 학습을 사용하는 패턴 일치와 경쟁할 수 있음을 알아냈다.
The Most Promising Sequence of Moves using Monte-Carlo Tree Search on a Small Go Board
이병두 (사)한국컴퓨터게임학회 2018 한국컴퓨터게임학회논문지 Vol.31 No.3
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.