Evaluation of Game Tree Search Methods by Game Records
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Kazunori Yamaguchi | Shogo Takeuchi | Tomoyuki Kaneko | Tomoyuki Kaneko | Shogo Takeuchi | K. Yamaguchi
[1] Arthur L. Samuel,et al. Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..
[2] Bruce Abramson,et al. Expected-Outcome: A General Model of Static Evaluation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[3] Bernd Brügmann Max-Planck. Monte Carlo Go , 1993 .
[4] Gerald Tesauro,et al. Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..
[5] Michael Buro,et al. From Simple Features to Sophisticated Evaluation Functions , 1998, Computers and Games.
[6] Matthew L. Ginsberg,et al. GIB: Steps Toward an Expert-Level Bridge-Playing Program , 1999, IJCAI.
[7] Jack van Rijswijck,et al. Learning from Perfection. A Data Mining Approach to Evaluation Function Learning in Awari , 2000, Computers and Games.
[8] Jonathan Schaeffer,et al. The challenge of poker , 2002, Artif. Intell..
[9] Martin Müller,et al. Computer Go , 2002, Artif. Intell..
[10] Brian Sheppard,et al. World-championship-caliber Scrabble , 2002, Artif. Intell..
[11] Michael Buro,et al. Improving heuristic mini-max search by supervised learning , 2002, Artif. Intell..
[12] Bruno Bouzy,et al. Monte-Carlo Go Developments , 2003, ACG.
[13] Michael Buro,et al. Evaluation Function Tuning via Ordinal Correlation , 2003, ACG.
[14] Andrew Tridgell,et al. Learning to Play Chess Using Temporal Differences , 2000, Machine Learning.
[15] Rich Caruana,et al. An Empirical Comparison of Supervised Learning Algorithms Using Different Performance Metrics , 2005 .
[16] Michael Buro,et al. Tuning evaluation functions by maximizing concordance , 2005, Theor. Comput. Sci..
[17] Olivier Teytaud,et al. Modification of UCT with Patterns in Monte-Carlo Go , 2006 .
[18] Akihiro Kishimoto,et al. Monte Carlo Go Has a Way to Go , 2006, AAAI.
[19] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[20] Rémi Coulom,et al. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.
[21] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[22] Kazunori Yamaguchi,et al. Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability , 2007, AAAI.
[23] Rémi Coulom,et al. Computing "Elo Ratings" of Move Patterns in the Game of Go , 2007, J. Int. Comput. Games Assoc..
[24] Kazunori Yamaguchi,et al. Evaluation of Monte Carlo tree search and the application to Go , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.
[25] Richard S. Sutton,et al. Sample-based learning and search with permanent and transient memories , 2008, ICML '08.
[26] David Silver,et al. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 × 9 Computer Go , 2022 .
[27] H. Jaap van den Herik,et al. Cross-Entropy for Monte-Carlo Tree Search , 2008, J. Int. Comput. Games Assoc..