CAREER: Establishing correctness of learning-enabled autonomous systems with conflicting requirements
Lead PI:
Tichakorn Wongpiromsarn
Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Autonomous systems are subject to multiple regulatory requirements due to their safety-critical nature. In general, it is infeasible to guarantee the satisfaction of all requirements under all conditions. In such situations, the system needs to decide how to prioritize among them. Two main factors complicate this decision. First, the priorities among the conflicting requirements may not be fully established. Second, the decision needs to be made under uncertainties arising from both the learning-based components within the system and the unstructured, unpredictable, and non-cooperating nature of the environments. Therefore, establishing the correctness of autonomous systems requires specification languages that capture the unequal importance of the requirements, quantify the violation of each requirement, and incorporate uncertainties faced by the systems.

Tichakorn Wongpiromsarn
Performance Period: 02/15/2022 - 01/31/2027
Institution: Iowa State University
Sponsor: NSF
Award Number: 2141153