Go is an extremely complex strategic board game despite its simple rules. Recently computer Go based on MCTS plays at human-master level and also has defeated top professional players with handicap games in 19×19 Go. Before implementing computer Go, in this paper we show weakness of pure MC algorithm for playing robust Tic-Tac-Toe game and present alternative method to make up the weakness. Furthermore we show how UCB algorithm works for balancing exploration and exploitation in game tree and discuss the need of a hybrid algorithm combined with UCB and strategy based MCTS, for implementing an enhanced computer Go.

We developed an efficient Monte-Carlo algorithm to solve dust-scattering radiative transfer problems for continuum radiation. The method calculates the scattered intensities for various anisotropic factors ( gi) all at once, while actual photon packets are tracked following a scattering phase function given by a single anisotropic factor ( g0). The algorithm was tested by applying the method to a dust cloud embedding a star at the cloud center and found to provide accurate results within the statistical fluctuation that is intrinsic in Monte-Carlo simulations. It was found that adopting g0 = 0.4 - 0.5 in the algorithm is most efficient. The method would be efficient in estimating the anisotropic factor of the interstellar dust by comparing the observed data with radiative transfer models.

몬테카를로 트리탐색은 최대우선탐색 알고리즘이며, 많은 게임 특히 바둑 게임에 성공적으로 적용되어 왔다. 삼목 게임에서 MCTS 간의 대국을 통해 성능을 평가하고자 했다. 첫 번째 대국 자는 항상 두 번째 대국자에 비해 압도적인 우위를 보였으며, 최선의 게임 결과가 무승부가 됨 에도 불구하고 첫 번째 대국자가 두 번째 대국자에 비해 우월한 이유를 찾고자 했다. MCTS는 반복적인 무작위 샘플링을 기반으로 하는 통계적 알고리즘이기 때문에, 특히 두 번째 대국자를 위해 전략을 요하는 시급한 문제를 적절히 대처하지 못한다. 이를 위해 전략적 MCTS(S-MCTS)를 제안하며, S-MCTS는 결코 삼목 게임에서 지지 않는다는 것을 보였다.

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.