Planning in the midst of chaos: how a stochastic Blood Bowl model can help to identify key planning features
暂无分享,去创建一个
[1] Peter I. Cowling,et al. Evolutionary MCTS for Multi-Action Adversarial Games , 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).
[2] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[3] Julian Togelius,et al. Blood Bowl: A New Board Game Challenge and Competition for AI , 2019, 2019 IEEE Conference on Games (CoG).
[4] R. Bellman. A Markovian Decision Process , 1957 .
[5] Leslie Pack Kaelbling,et al. On the Complexity of Solving Markov Decision Problems , 1995, UAI.
[6] Julian Togelius,et al. Artificial Intelligence and Games , 2018, Springer International Publishing.
[7] Yishay Mansour,et al. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes , 1999, Machine Learning.
[8] Sabarinath Mohandas,et al. A.I for Games with High Branching Factor , 2018, 2018 International CET Conference on Control, Communication, and Computing (IC4).
[9] Shivakumar Vaithyanathan,et al. Generalized Model Selection for Unsupervised Learning in High Dimensions , 1999, NIPS.
[10] Andreas Gustafsson. Winner Prediction of Blood Bowl 2 Matches with Binary Classification , 2019 .
[11] Julian Togelius,et al. Online Evolution for Multi-action Adversarial Games , 2016, EvoApplications.
[12] Janet Wiles,et al. The challenge of Go as a domain for AI research: a comparison between Go and chess , 1995, Proceedings of Third Australian and New Zealand Conference on Intelligent Information Systems. ANZIIS-95.