NSF Young Investigator

自动规划作为人工智能的一个分支学科，已经有三十多年的历史了。虽然该领域的正式基础已经变得越来越复杂，但在将人工智能规划技术应用于现实问题方面，进展缓慢。造成这种状况的一个重要原因是对规划中的搜索控制和效率问题缺乏足够的了解。因此，PI建议开发系统的方法来提高人工智能规划技术的效率，以便于将其应用于现实的规划任务。具体来说，他将(1)研究规划算法中的基本设计权衡，并利用这种理解来设计更高效的规划器；(2)开始一个多管齐下的项目，将加速学习技术，包括计划重用、搜索控制规则、衍生重播和抽象集成到部分阶规划框架中；(3)开发混合规划架构，能够促进规划者和其他类型的特定领域专家（包括人类）之间的结构化交互。通过工业和跨学科合作，他建议在现实的规划任务中评估产生的规划框架，如制造规划。

Automatic planning, as a sub-discipline of AI, has been around for over thirty years. While formal foundations of the field have grown increasingly sophisticated, progress has been slow in terms of applications of AI planning techniques to realistic problems. An important reason for this state of affairs is the lack of adequate understanding of search-control and efficiency issues in planning. Accordingly, the PI proposes to develop systematic approaches for improving efficiency of AI planning techniques, so as to facilitate their application to realistic planning tasks. Specifically, he will (1) study the fundamental design tradeoffs in planning algorithms, and use that understanding to design more efficient planners, (2) start a multi-pronged project to integrate speedup learning techniques, including plan reuse, search control rules, derivational replay and abstraction into partial-order planning framework, and (3) develop hybrid planning architectures capable of facilitating structured interaction between planners and other types of domain-specific specialists, including humans. Through industrial and inter-disciplinary collaborations, he proposes to evaluate the resulting planning frameworks in realistic planning tasks such as manufacturing planning.