Search-Based Repair of DNN Controllers of AI-Enabled Cyber-Physical Systems Guided by System-Level Specifications

Deyun Lyu, Zhenya Zhang, Paolo Arcaini, Fuyuki Ishikawa, Thomas Laurent, Jianjun Zhao

Abstract: In AI-enabled CPSs, DNNs are used as controllers for the physical system. Despite their advantages, DNN controllers can produce wrong control decisions, which can lead to safety risks for the system. Once wrong behaviors are detected, the DNN controller should be fixed. DNN repair is a technique that allows to perform this fine-grained improvement. However, state-of-the-art DNN repair techniques require ground-truth labels to guide the repair. For AI-enabled CPSs, these are not available, as it is not possible to assess whether a specific control decision is correct. Nevertheless, it is possible to assess whether the DNN controller leads to wrong behaviors of the controlled system by considering system-level requirements. In this paper, following this observation, we propose a novel DNN repair approach that is guided by system-level specifications. The approach takes in input a system-level specification, some tests violating the specification, and some faulty DNN weights. The approach searches for alternative weight values with the goal of fixing the behavior on the failing tests without breaking the passing tests. We also propose a heuristic that allows us to accelerate the search by avoiding the execution of some tests. Experiments on real-world AI-enabled CPSs show that the approach effectively repairs their controllers.