• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.217-223
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• 2011

AI(Artificial Intelligence) refers to the area of computer engineering and IT technology that focuses on the methodology and creation of intelligent agents. The Othello game is often produced with AI, since it is played with relatively simple rules on a board and on a limited space of 8 rows and 8 columns. Previous algorithms take longer time than desirable and often fail to face new circumstances, as they search for all the possible cases and rules. In order to solve this crucial weakness, we propose that a CBR algorithm be applied to Orthello. Case-Based Reasoning(CBR), is the process of solving new problems based on the solutions of the past similar problems. We can apply this process to Othello and expedite the process of computer reasoning for a solution to new cases based on the data from accumulated past cases. Then, these new solutions are dynamically added to the set of past cases so that it becomes harder for players(users) to be able to read the pattern. The proposed system in which a CBR algorithm is applied to the Othello game makes the computation process faster and the game harder to play.

• Journal of IKEEE
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• v.23 no.4
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• pp.1187-1194
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• 2019

This paper proposes a CNN architecture as value function network of an artificial intelligence Othello game agent and its learning scheme using reinforcement learning algorithm. We propose an approach to construct the value function network by using CNN to learn the records of professional players' real game and an approach to enhance the network parameter by learning from self-play using reinforcement learning algorithm. The performance of value function network CNN was compared with existing ANN by letting two agents using each network to play games each other. As a result, the winning rate of the CNN agent was 69.7% and 72.1% as black and white, respectively. In addition, as a result of applying the reinforcement learning, the performance of the agent was improved by showing 100% and 78% winning rate, respectively, compared with the network-based agent without the reinforcement learning.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.598-600
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• 2004

기존의 연구 논문 중 비결정론적인 알고리즘인 유전자 알고리즘이나 인공신경망 등을 오델로 게임에 적용하여 자동학습을 시킨 예는 많으나 면역알고리즘을 모델로 게임에 적용한 예는 찾기가 어렵다 본 논문에서는 생리학의 면역시스템의 특징을 그대로 적용한 면역알고리즘을 모델로 게임에 적용하여 게임전략 생성에 관하여 연구한다. 생리학의 면역시스템은 자기조절능력이 있다는 외과 재 감염시 빠르게 대응할 수 있다는 특징이 있다. 면역알고리즘을 이용하여 탐색된 전략을 유전자알고리즘 그리고 기존에 연구되어진 게임전략 등과 실험하여 그 결과를 비교.연구한 결과 면역알고리즘을 적용하여 탐색된 모델로 게임전략이 가장 높은 승률을 보인다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.17-25
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• 2006

An intelligent game has been studied for some time, and the main purpose of the study was to win against human by enhancing game skills. But some commercial games rather focused on adaptation of the user's behavior in order to bring interests on the games. In this study, we are suggesting an adaptive reinforcement learning algorithm, which focuses on the adaptation of user behavior. We have designed and developed the Othello game, which provides large state spaces. The evaluation of the experiment was done by playing two reinforcement learning algorithms against Min-Max algorithm individually. And the results show that our approach is playing more improved learning rate, than the previous reinforcement learning algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.778-780
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• 2005

최근 인공지능의 기법을 도입한 게임에 관한 연구가 활발히 진행되고 있다. 특히 신경망의 역 전파 알고리즘을 적용한 게임은 구현이 용이하고 학습이 완료되면 비교적 실행이 빨라서 많은 연구가 진행되고 있지만 기본적인 학습시간이 길고 최적화에 관한 문제점이 존재하고 있다. 이러한 문제점을 개선하고자 본 논문에서는 기존의 역 전파 알고리즘과 강화학습의 Q-learning알고리즘을 모델로 게임에 적용하여 비교 분석 하였다. 실험은 단순한 min-max 알고리즘과 각각 대결하여 승수 와 승율을 중심으로 비교하였고 실험의 결과는 강화학습의 알고리즘이 역 전파 알고리즘에 비하여 비교적 우수한 결과를 제시하였다.