![]() ![]() Iqbal, A.: Increasing efficiency and quality in the automatic composition of three-move mate problems. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG 2013), pp. Ikeda, K., Viennot, S.: Production of various strategies and position control for Monte-Carlo Go - entertaining human players. In: Plaat, A., van den Herik, J., Kosters, W. Ikeda, K., Shishido, T., Viennot, S.: Machine-learning of shape names for the game of Go. ![]() In: AAAI-04 Workshop on Challenges in Game Artificial Intelligence, pp. Hunicke, R., Chapman, V.: AI for dynamic difficulty adjustment in games. Hirose, M., Ito, T., Matsubara, H.: Automatic composition of Tsume-shogi by reverse method. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017), pp. Äemediuk, S., Tamassia, M., Raffe, W.L., Zambetta, F., Li, X., Mueller, F.: Monte Carlo tree search based algorithms for dynamic difficulty adjustment. KeywordsÄe Kegel, B., Haahr, M.: Procedural puzzle generation: a survey. The result demonstrates the possibility of using automatically generated problems to assist human players in improving their playing skills. Experiments show that the overall ability for beginners to complete T-spin during play is improved after trained by the given problems. To assist beginners in mastering the technique, automatically generated two-step to T-spin problems are given for them to solve. More specifically, a powerful technique called T-spin is hard for beginners to learn. ![]() In this paper, automatically generated puzzles are used to assist human players in improving the playing skills for the game of Tetris, a famous and popular tile-matching game. One of the branches is procedural content generation, aiming to generate game contents such as maps, stories, and puzzles automatically. Researchers in the field of computer games interest in creating not only strong game-playing programs, but also programs that can entertain or teach human players. ![]()
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