Grail: A Framework for Adaptive and Believable AI in Video Games
暂无分享,去创建一个
[1] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[2] John E. Laird,et al. Research in human-level AI using computer games , 2002, CACM.
[3] Jing Dong,et al. Event-based blackboard architecture for multi-agent systems , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.
[4] Barbara Hayes-Roth,et al. A Blackboard Architecture for Control , 1985, Artif. Intell..
[5] Michael Buro,et al. RTS Games and Real-Time AI Research , 2003 .
[6] Jeff Orkin,et al. Applying Goal-Oriented Action Planning to Games , 2008 .
[7] Juan Julián Merelo Guervós,et al. Optimizing player behavior in a real-time strategy game using evolutionary algorithms , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).
[8] Dominik Slezak,et al. Granular Games in Real-Time Environment , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).
[9] Michele Pirovano,et al. Fuzzy Tactics: A scripting game that leverages fuzzy logic as an engaging game mechanic , 2014, Expert Syst. Appl..
[10] Steven Rabin,et al. Game AI Pro 2: Collected Wisdom of Game AI Professionals , 2013 .
[11] Nathan R. Sturtevant,et al. TBA*: Time-Bounded A* , 2009, IJCAI.
[12] Andrzej Janusz,et al. Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).
[13] Dave Mark,et al. Behavioral Mathematics for Game AI , 2009 .
[14] Ryan B. Hayward,et al. Monte Carlo Tree Search in Hex , 2010, IEEE Transactions on Computational Intelligence and AI in Games.
[15] Bart De Schutter,et al. A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[16] P. Hingston. Believable Bots: Can Computers Play Like People? , 2012 .