EvoMCTS: Enhancing MCTS-based players through genetic programming
暂无分享,去创建一个
[1] Moshe Sipper,et al. Evolving board-game players with genetic programming , 2011, GECCO.
[2] E. Berlekamp,et al. Winning Ways for Your Mathematical Plays , 1983 .
[3] Yngvi Björnsson,et al. Simulation-Based Approach to General Game Playing , 2008, AAAI.
[4] Risto Miikkulainen,et al. Discovering Complex Othello Strategies through Evolutionary Neural Networks , 1995, Connect. Sci..
[5] Moshe Sipper,et al. Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess , 2007, EuroGP.
[6] Moshe Sipper,et al. GP-Gammon: Genetically Programming Backgammon Players , 2005, Genetic Programming and Evolvable Machines.
[7] George C. Williams,et al. Adaptation and Natural Selection , 2018 .
[8] Jonathan Schaeffer,et al. Checkers Is Solved , 2007, Science.
[9] R. Lewontin. ‘The Selfish Gene’ , 1977, Nature.
[10] Yngvi Björnsson,et al. CadiaPlayer: A Simulation-Based General Game Player , 2009, IEEE Transactions on Computational Intelligence and AI in Games.
[11] Moshe Sipper,et al. Evolving players that use selective game-tree search with genetic programming , 2012, GECCO '12.
[12] David J. Montana,et al. Strongly Typed Genetic Programming , 1995, Evolutionary Computation.
[13] Donald E. Eastlake,et al. The Greenblatt chess program , 1967, AFIPS '67 (Fall).
[14] T. Cazenave. Evolving Monte-Carlo Tree Search Algorithms , 2007 .
[15] Moshe Sipper,et al. GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players , 2005, EuroGP.
[16] Moshe Sipper,et al. Evolving Lose-Checkers players using genetic programming , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.
[17] Kai-Fu Lee,et al. The Development of a World Class Othello Program , 1990, Artif. Intell..
[18] Rémi Coulom,et al. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search , 2006, Computers and Games.
[19] Paul S. Rosenbloom,et al. A World-Championship-Level Othello Program , 1982, Artif. Intell..
[20] Jonathan Schaeffer,et al. CHINOOK: The World Man-Machine Checkers Champion , 1996, AI Mag..
[21] W. Daniel Hillis,et al. Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .
[22] H. Jaap van den Herik,et al. Progressive Strategies for Monte-Carlo Tree Search , 2008 .
[23] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[24] Simon M. Lucas,et al. Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board go , 2005, IEEE Transactions on Evolutionary Computation.
[25] Ryan B. Hayward,et al. MOHEX Wins Hex Tournament , 2012, J. Int. Comput. Games Assoc..
[26] Sean Luke,et al. Code Growth Is Not Caused by Introns , 2000 .
[27] Olivier Teytaud,et al. Modification of UCT with Patterns in Monte-Carlo Go , 2006 .
[28] Ryan B. Hayward,et al. Monte Carlo Tree Search in Hex , 2010, IEEE Transactions on Computational Intelligence and AI in Games.
[29] Michael Buro,et al. Improving heuristic mini-max search by supervised learning , 2002, Artif. Intell..
[30] Kenneth O. Stanley,et al. Evolving neural networks for geometric game-tree pruning , 2011, GECCO '11.