• Journal of Korea Game Society
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• v.16 no.4
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• pp.79-86
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• 2016

Monte-Carlo Tree Search(MCTS) is a best-first tree search algorithm and has been successfully applied to various games, especially to the game of Go. We evaluate the performance of MCTS playing against each other in the game of Tic-Tac-Toe. It reveals that the first player always has an overwhelming advantage to the second player; and we try to find out the reason why the first player is superior to the second player in spite of the fact that the best game result should be a draw. Since MCTS is a statistical algorithm based on the repeated random sampling, it cannot adequately tackle an urgent problem that needs a strategy, especially for the second player. For this, we propose a strategic MCTS(S-MCTS) and show that the S-MCTS player never loses a Tic-Tac-Toe game.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.115-122
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• 2012

We present here a new class of activation functions for neural networks, which herein will be called CosGauss function. This function is a cosine-modulated gaussian function. In contrast to the sigmoidal-, hyperbolic tangent- and gaussian activation functions, more ridges can be obtained by the CosGauss function. It will be proven that this function can be used to aproximate polynomials and step functions. The CosGauss function was tested with a Cascade-Correlation-Network of the multilayer structure on the Tic-Tac-Toe game and iris plants problems, and results are compared with those obtained with other activation functions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1228-1234
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• 2007

In this paper, we develop the thinking extension education program for the gifted students of information and prove the validity and effectiveness of the proposed model by presenting the Tic-tac-toe problem as the practical example of the information-gifted students. This model consists of four phases which has the game tree as data structure and the search of game lee as control structure. And the search of game tree becomes the basis of the thinking extension education program. This model gives the help for students to learn representing the problem as tree structure and solving the problem of tree structure using the search method of game tree. The internal ability of the information-gifted for thinking extension of this education program contains the fluency, perceptiveness, originality, power of concentration, imaginative power, analyzing skills, pattern recognition, space sense, synthesizing, problem-solving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.310-314
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• 2007

In this paper, we develop the thinking extension education program for the gifted students of information, and prove the validity and effectiveness of the proposed model by presenting the Tic-tac-toe problem as the practical example of the information-gifted students. This model consists of four phases which has the game tree as data structure and the search of game tree as control structure. And the search of game tree becomes the basis of the thinking extension education program. This model gives the help for students to learn representing the problem as tree structure and solving the problem of tree structure using the search method of game tree. The internal ability of the information-gifted for thinking extension of this education program contains the fluency, perceptiveness, originality, power of concentration, imaginative power, analyzing skills, pattern recognition, space sense, synthesizing, problem-solving.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.109-118
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• 2015

The game of Go originated from ancient China is regarded as one of the most difficult challenges in the filed of AI. Over the past few years, the top computer Go programs based on MCTS have surprisingly beaten professional players with handicap. MCTS is an approach that simulates a random sequence of legal moves until the game is ended, and replaced the traditional knowledge-based approach. We applied the UCT algorithm which is a MCTS variant to the game of Tic-Tac-Toe for finding the best first move, and compared it with the result generated by a pure MCTS. Furthermore, we introduced and compared the performances of epsilon-Greedy algorithm and UCB algorithm for solving the Multi-Armed Bandit problem to understand the UCB.