RI: Small: A Human-Level, Real-Time, Integrated Agent

该项目正在开发和集成人工智能的统计和符号方法在代理架构和评估代理在竞争领域，特别是实时战略游戏星际争霸。实时策略（RTS）游戏提供了一些有趣的研究挑战，包括实时决策，巨大的状态空间和不完美的信息。星际争霸是一款流行的商业RTS游戏，拥有多个专业游戏联盟，因此非常适合评估AI代理的性能。职业星际争霸玩家在多个抽象层次上对战略决策进行推理和反应，有时每分钟执行超过300个游戏动作，因此开发竞赛级星际争霸代理提出了非凡的挑战。 更具体地说，该项目正在使用新的监督和无监督学习算法，从专业游戏轨迹的集合中自动学习领域知识;该代理正在反应式规划架构ABL（行为语言）中实现。ABL反应式规划器为在实时执行环境中集成多个异构推理器提供了粘合剂。这项工作预计将作出重大贡献的理解决策过程中的一个复杂的，实时域。这种理解将有助于开发可在现实环境中部署的强大智能系统。这项工作将激励人工智能研究人员构建集成的代理架构。作为一款具有非常高水平职业玩法的知名游戏，《星际争霸》AI的研究有可能吸引人们对AI研究的极大关注。由我们实验室主办的星际争霸竞赛吸引了学术界内外，以及高中、本科和研究生的极大兴趣。因此，这项工作有可能提高公众对人类AI研究的认识，并鼓励高中生从事计算机科学和游戏设计。

This project is developing and integrating statistical and symbolic methods of Artificial Intelligence in an agent architecture and evaluating the agent in a competitive domain, notably the real-time strategy game StarCraft. Real-time strategy (RTS) games provide several interesting research challenges including real-time decision making, enormous state spaces and imperfect information. StarCraft is a popular commercial RTS game that has several professional gaming leagues, and therefore ideal for evaluating the performance of AI agents. Professional StarCraft players reason about and react to strategic decisions at multiple levels of abstraction, sometimes executing over 300 game actions per minute, so developing competition-level StarCraft agents presents extraordinary challenges. More specifically, the project is using novel supervised and unsupervised learning algorithms to automatically learn domain knowledge from collections of professional gameplay traces; the agent is being implemented within the reactive planning architecture ABL (A Behavior Language). The ABL reactive planner provides the glue for integrating multiple, heterogeneous reasoners within a real-time execution environment. This work is expected to make significant contributions to the understanding of decision making processes in a complex, real-time domain. This understanding will contribute to the development of robust, intelligent systems that can be deployed within real-world environments. This work will motivate AI researchers to build integrated agent architectures. As a well-known game with very high-level professional play, research in StarCraft AI has the potential to attract significant attention to AI research. The StarCraft competition being hosted by our lab has attracted significant interest both within and outside academia, and at the high-school, undergraduate and graduate level. Thus, this work has the potential to raise general public awareness in research in human-level AI, and will encourage high-school students to pursue careers in computer science and game design.