RI: Small: Sound Abstractions for Efficient and Reliable Automated Planning

自主系统有可能通过帮助人们从事从家庭老人护理到空间探索等活动来改变社会。这样的代理需要具有足够的通用性，以解决一系列任务，而无需事先进行特定于任务的编程。 然而，很难计算自主代理为了实现新目标（例如，以寻找具有高水分含量的岩石）。该项目将开发一个新的框架和算法，利用抽象的原则，有效地计算任务特定的行为，在一个独立于域的方式自治代理。 由于抽象通常会模糊细节，因此这一原则很难在实践中使用-使用抽象模型计算的计划可能会错过细节，并且在现实世界的自治系统中使用时可能会很危险。本项目将开发新的方法，跟踪抽象产生的不精确性，并有效地解决它，以有效地计算可靠的计划。更准确地说，本项目将开发一个正式的框架，用于分析和创建合理的抽象模型，即，它们只允许关于可用的最精确模型的正确推断（并且可能是正确推断的子集）。一般来说，这样的模型区分模型的不精确性所造成的抽象从非确定性或随机性固有的领域。 它将使用合理的抽象模型开发有效规划的新范例，同时保持可证明的正确性，以获得最准确的模型。这些规划范例解决了不确定性和不确定性的设置;它们还将允许人工智能系统通过自动组合规划器来计算复杂的计划，这些规划器是独立设计的，可以有效地处理不同类型的抽象模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估而被认为值得支持。

Autonomous systems have the potential to transform society by assisting people in activities ranging from household elder care to space exploration. Such agents need to be versatile enough to solve a range of tasks without prior task-specific programming. However, it is difficult to compute the behavior that an autonomous agent should execute in order to achieve a new objective (e.g., to search for rocks with high moisture content). This project will develop a new framework and algorithms that utilize the principle of abstraction to efficiently compute task-specific behavior for autonomous agents in a domain-independent fashion. Since abstraction blurs detail in general, this principle has been difficult to employ in practice -- plans computed using abstract models can miss details, and can be dangerous to use with real-world autonomous systems. This project will develop new methods that keep track of imprecision created by abstraction and effectively resolve it, to efficiently compute reliable plans.More precisely, this project will develop a formal framework for analyzing and creating abstract models that are sound, i.e., they permit only correct inferences (and possibly a subset of correct inferences) with regard to the most accurate model available. In general, such models distinguish model imprecision caused by abstraction from non-determinism or stochasticity inherent in the domain. It will develop new paradigms for efficient planning using sound abstract models while remaining provably correct with regard to the most accurate model available. These planning paradigms address settings with and without uncertainty; they will also allow AI systems to compute complex plans by automatically composing planners that are independently designed to be efficient for different types of abstract models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.