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Deduction of fighting-game countermeasures using the k-nearest neighbor algorithm and a game simulator.

, , , and . CIG, page 1-5. IEEE, (2014)

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Efficient implementation of breadth first search for general video game playing., , , , and . GCCE, page 1-2. IEEE, (2016)Investigating Kinect-based fighting game AIs that encourage their players to use various skills., , , , , and . GCCE, page 334-335. IEEE, (2015)Position-based reinforcement learning biased MCTS for General Video Game Playing., , , and . CIG, page 1-8. IEEE, (2016)Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models., , , , and . CoRR, (2016)Combining pathfmding algorithm with Knowledge-based Monte-Carlo tree search in general video game playing., , , , and . CIG, page 523-529. IEEE, (2015)Predicting the opponent's action using the k-nearest neighbor algorithm and a substring tree structure., , , , and . GCCE, page 533-534. IEEE, (2015)Piano learning application with feedback provided by an AR virtual character., , , and . GCCE, page 1-2. IEEE, (2016)Procedural generation of angry birds levels that adapt to the player's skills using genetic algorithm., , , and . GCCE, page 535-536. IEEE, (2015)Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI., , , , and . ACE, page 27:1-27:6. ACM, (2016)Procedural generation of angry birds levels with adjustable difficulty., , , and . CEC, page 1311-1316. IEEE, (2016)