CAREER: Generalizable and Reliable Behavior Synthesis in Uncertain Open-World Environments

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There is broad consensus on the need for AI systems that are reliable and useful in situations going beyond carefully controlled environments. The focus of this project is on developing autonomous agents that can plan and act safely in “open-world” settings, where the agent has limited information about the environment where it will be used. Such agents may be uncertain about the numbers, types and identities of objects that they may encounter, as well as about the relationships between them. Furthermore, the nature of uncertainty about these properties may be “non-stationary”, meaning the environment may change during the agent’s deployment. The outcomes of this project will help increase the scope and applicability of AI systems by developing new methods for computing safe and reliable AI behavior in realistic non-stationary, open-world settings. In order to make AI systems more broadly accessible, this project will also develop an autonomous interactive tutorial system for teaching students about different types of AI planning problems and their solution representations. The proposed activity will develop new principles and analytical methods for understanding the computational nature of open-world planning problems. It will engender broad convergence of principles and algorithms from logic-based and probabilistic approaches to AI, as well as from theoretical computer science. In particular, it will develop new representations for efficiently expressing qualitative and decision-theoretic formulations of open-world planning problems along with efficient algorithms and implementations for solving them while using abstractions for efficiency and generalizability. New methods will be developed to utilize statistical learning techniques for enhancing computational efficiency while ensuring that the computed agent behavior meets desired requirements on safety and reliability in open-world settings. The results and progress made during the project will be evaluated on physical and simulated testbeds featuring contemporary robotics platforms. Problem generators and simulated testbeds will be made publicly available as benchmarks to aid reproducibility and spur progress in this area of research.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.