Monte Carlo Tree Search (MCTS) has been successfully applied to a variety of games. Its best-first algorithm enables implementations without evaluation functions. Combined with Upper Confidence bounds applied to Trees (UCT), MCTS has an advantage over traditional depth-limited minimax search with alpha-beta pruning in games with high branching factors such as Go. However, minimax search with alpha-beta pruning still surpasses MCTS in domains like Chess. Studies show that MCTS does not detect shallow traps, where opponents can win within a few moves, as well as minimax search. Thus, minimax search performs better than MCTS in games like Chess, which can end instantly (king is captured). A combination of MCTS and minimax algorithm is proposed in this thesis to see the effectiveness of detecting shallow traps in Go problems.