"Search, Opponent Modelling, Cooperation, and State Inference in Complex Imperfect Information Domains."

应用于游戏的人工智能（AI）研究有着悠久的传统， 艾伦·图灵在计算机国际象棋上的研究已经有75年了学习AI算法的优势 在这方面，游戏是精确定义的，与现实世界相比相对较小， 决策领域，但足够复杂，提出坚韧研究问题，其解决方案， 帮助我们创造出具有人类智能的机器。人工智能研究在游戏中取得了成功 比如国际象棋、西洋双陆棋和跳棋--现在机器在这些方面的表现与最好的不相上下，甚至更好 人类玩家然而，在更复杂的领域，机器仍然落后于人类。 专家与人工智能研究非常成功的游戏的主要区别在于， 游戏状态信息对玩家隐藏或者移动选择的数量非常大。两 属性呈现完全枚举，这是许多高性能AI系统的基石， 效率较低。 这里提出的研究目标是创建将达到或超过 人类专家在具有不确定性、不完美性和不确定性的实时决策领域中的表现 信息或复杂的状态和动作空间。我们将开发的基准应用程序 是基于技巧的纸牌游戏和即时战略视频游戏。改进现有技术 要求我们开发新的算法，可以模拟对手，推断隐藏的游戏状态， 与合作伙伴，并在抽象的搜索空间中向前看。从长远来看，这样做的结果 该项目将增加我们对人工智能领域需要解决的基本问题的理解。 创建人类级别的AI系统的过程。在短期内，我们预计电脑游戏 行业受益于我们的研究，因为它需要可靠的计算机控制代理 在我们研究的领域。

Artificial intelligence (AI) research applied to games has a long tradition that reaches back at least 75 years with Alan Turing's work on computer chess. The advantage of studying AI algorithms in this area is that games are precisely defined, relatively small when compared to real-world decision domains, and yet sufficiently complex to pose tough research problems whose solutions can help us create machines of human-level intelligence. AI research has had its successes in games like chess, backgammon, and checkers - where machines now play on par with or better than the best human players. However, in more complex domains machines are still trailing behind human experts. The main differences to the games in which AI research has been very successful are that game state information is hidden from players or the number of move choices is very large. Both properties render complete enumeration, which is a cornerstone of many high-performance AI systems, less effective. The objective of the research proposed here is to create systems that will reach or surpass the performance of human experts in real-time decision domains that feature uncertainty, imperfect information, or complex state and action spaces. The benchmark applications we will be working on are trick-based card games and real-time strategy video games. Improving the state of the art requires us to develop new algorithms that can model opponents, infer hidden game states, cooperate with partners, and look-ahead in abstracted search spaces. In the long term, the results of this project will increase our understanding of fundamental AI problems that need to be solved in the process of creating human-level AI systems. In the short term, we anticipate the computer games industry benefiting from our research, because it is in need of credible computer controlled agents in the domains we study.