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

该项目正在开发和集成人工智能在代理架构中的统计和符号方法，并在竞争领域评估代理，特别是实时战略游戏《星际争霸》。即时战略（RTS）游戏提供了许多有趣的研究挑战，包括实时决策制定、巨大的状态空间和不完全信息。《星际争霸》是一款广受欢迎的商业RTS游戏，它拥有多个职业游戏联盟，因此非常适合评估AI代理的表现。专业的《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，所以开发竞技级别的《星际争霸》代理是非常具有挑战性的。更具体地说，该项目使用新颖的监督式和非监督式学习算法，从专业玩法痕迹的集合中自动学习领域知识；代理在响应式计划体系结构ABL（行为语言）中实现。ABL响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。这项工作有望对理解复杂、实时领域的决策过程做出重大贡献。这种理解将有助于开发健壮的智能系统，这些系统可以部署在现实环境中。这项工作将激励人工智能研究人员构建集成的代理架构。作为一款拥有高水平专业玩法的知名游戏，对《星际争霸》AI的研究有可能引起人们对AI研究的极大关注。我们实验室举办的《星际争霸》竞赛吸引了学术界内外、高中、本科生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级别AI研究的认识，并将鼓励高中生从事计算机科学和游戏设计方面的职业。

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.