RI: Medium: Hierarchical Decision Making for Physical Agents

该奖项的研究解决了智能系统开发中的一个核心问题：在复杂的状态空间中，在相当长的时间尺度上生成有效的、有意的活动。尽管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环境中的非常重复的行为，但研究人员正在开发一种新的分层决策的数学框架。该框架基于针对高级动作的所谓的“天使”不确定性语义，支持能够生成可证明是最优的高级计划的新算法，而无需将这些计划扩展为原始动作。因此，这些算法满足“向下精化性质”，解决了一个在人工智能规划研究的核心已经开放了30多年的问题。通过将线下和在线的分级审议与分级强化学习和学徒学习相结合，以获得低水平的身体技能，该研究试图使灵巧的人类规模的机器人能够在非结构化环境中操作，如厨房、办公室和更复杂的环境。这项研究承诺对社会、政府、企业和军事领域的大规模决策有更深入的理解，并为其提供更好的工具。该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。

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).