RI: Medium: Hierarchical Decision Making for Physical Agents

该奖项下的研究解决了智能系统开发中的一个核心问题：在复杂状态空间中，在相当长的时间尺度上产生有效的、有意识的活动。鉴于目前的计划生成方法局限于非常短的计划或高度结构化和简化的环境中非常重复的行为，研究人员正在开发一个新的数学框架分层决策。该框架基于高级动作的所谓“天使般”非确定性语义，支持能够生成可证明最优的高级计划而无需将这些计划扩展为原始动作的新算法。因此，这些算法满足了“向下细化属性”，解决了人工智能规划研究核心30多年来一直存在的问题。通过将离线和在线的分层审议与分层强化学习和学徒学习相结合，以获得低水平的身体技能，该研究旨在使灵巧的人类规模机器人能够在非结构化环境中运行，如厨房，办公室和更复杂的环境。这项研究有望产生更深入的理解，更好的工具，大规模的决策一般，在社会，政府，企业和军事领域的影响。该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。

Research under this award addresses a core problem in the development of intelligent systems: the generation of effective, deliberate activity over substantial time scales in complex state spaces. Whereas current methods for plan generation are limited either to very short plans or to very repetitive behavior in highly structured and simplified environments, the investigators are developing a new mathematical framework for hierarchical decision making. The framework, based on so-called "angelic" nondeterministic semantics for high-level actions, underpins new algorithms capable of generating provably optimal high-level plans without expanding those plans into primitive actions. The algorithms therefore satisfy the "downward refinement property," resolving a problem that has been open for over 30 years at the heart of AI planning research. By combining hierarchical deliberation, both offline and online, with hierarchical reinforcement learning and apprenticeship learning for low-level physical skill acquisition, the research seeks to enable dexterous human-scale robots to operate in unstructured environments such as kitchens, offices, and environments of still greater complexity. The research promises to yield a deeper understanding of, and better tools for, large-scale decision making in general, with implications in the social, governmental, corporate, and military spheres. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).