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.
