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NTIS 바로가기융합정보논문지 = Journal of Convergence for Information Technology, v.11 no.3, 2021년, pp.1 - 6
박대근 (공주대학교 게임디자인학과) , 이완복 (공주대학교 게임디자인학과)
Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study prop...
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E. Poromaa. (2017. Crushing Candy Crush : Predicting Human Success Rate in a Mobile Game using Monte-Carlo Tree Search. Student thesis. KTH.
R. Coulom. (2006). Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. 5th International Conference on Computer and Games. May 29-31.
D. Silver. (2016). Mastering the game of Go with deep neural networks and tree search. Nature. 529(7587). 484-489.
A. Andelkovic. (2018). Using Artificial Intelligence to Test the Candy Crush Saga Game. Alexander Andelkovic. comaqa. (Online). https://www.youtube.com/watch?v4xECMpgeOxE/
D. Silver. (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Science. 362, 1140-1144.
L. Kocsis & C. Szepesvari. (2006). Bandit based monte-carlo planning. In European conference on machine learning (pp. 282-293). Springer, Berlin, Heidelberg.
S. Gelly, Y. Wang, R. Munos & O. Teytaud. (2006). Modification of UCT with Patterns in Monte-Carlo Go. Computer Science.
Aiandgames. (2018). Monte-Carlo Tree Search in TOTAL WAR: ROME II's Campaign AI. aiandgames. (Online). https://aiandgames.com/revolutionary-warfare-the-ai-of-total-war-part-3/
M. V. Otterlo & M. A. Wiering. (2012). Reinforcement learning and markov decision processes. In Reinforcement learning (pp. 3-42). Springer, Berlin, Heidelberg. DOI : 10.1007/978-3-642-27645-3_1
F. S. Melo, (2007). Convergence of Q-learning: a simple proof. Proceedings of the European Control Conference 2007. 2-5.
R. Bellman. (1957). A Markovian Decision Process. Journal of Mathematics and Mechanics. 6(5). 679-684.
M. Tokic & G. Palm. (2011). Value-Difference Based Exploration: Adaptive Control Between Epsilon-Greedy and Softmax. Advances in Artificial Intelligence, Lecture Notes in Computer Science. 7006. 335-346 DOI : 10.1007/978-3-642-24455-1_33
S. Purmonen, (2017). Predicting Game Level Difficulty Using Deep Neural Networks. Student thesis of KTH.
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