Planning and state evaluation in real-time decision domains

人工智能(AI)研究应用于游戏有着悠久的传统，至少可以追溯到70年前艾伦·图灵对计算机国际象棋的研究。在这一领域研究人工智能算法的优势在于，游戏定义准确，与现实世界的决策域相比相对较小，而且还足够复杂，足以提出棘手的研究问题，其解决方案可以帮助我们创造出人类水平的智能机器。人工智能研究在国际象棋、奥赛罗、双陆棋和跳棋等游戏中取得了成功--在这些游戏中，机器现在可以与最优秀的人类棋手平起平坐，甚至更好地发挥作用。然而，巨大的挑战依然存在--最明显的是在古老的亚洲棋类游戏围棋中，迄今为止还没有哪种机器能与优秀初学者的实力相提并论。同样，视频游戏AI系统的学习、规划和推理能力也落后于人类玩家。本项目的目标是通过抽象、前瞻性搜索和搜索启发式的机器学习来提高AI系统在实时决策领域的决策质量。我们的应用重点将是大众游戏，在这些游戏中，初学者的表现仍然好于最好的程序。这个项目的结果将增加我们对基本人工智能问题的理解，并将对一般的实时控制领域，特别是需要可信的计算机控制代理的计算机游戏行业产生相当大的影响。

Artificial intelligence (AI) research applied to games has a long tradition thatreaches back at least 70 years with Alan Turing's work on computer chess. Theadvantage of studying AI algorithms in this area is that games are preciselydefined, relatively small when compared to real-world decision domains, and yetsufficiently complex to pose tough research problems whose solutions can help uscreating machines of human-level intelligence. AI research has had its successesin games like chess, Othello, backgammon, and checkers - where machines now playon par with or better than the best human players. However, big challenges remain- most notably in the ancient Asian board game of Go for which to date no machinecan match the strength of good beginners. Likewise, video game AI systems arelacking learning, planning, and reasoning capabilities which results in weakperformance compared to human players.  The objective of this project is to improve the decision quality of AI systemsin real-time decision domains by means of abstractions, look-ahead search, andmachine learning of search heuristics. Our application focus will be on populargames in which beginners still outperform the best programs. The results of thisproject will increase our understanding of fundamental AI problems and will haveconsiderable impact on the real-time control domain in general and the computergames industry in particular, which is in need of credible computer controlledagents.