4 research outputs found
Causal Repair of Learning-enabled Cyber-physical Systems
Models of actual causality leverage domain knowledge to generate convincing
diagnoses of events that caused an outcome. It is promising to apply these
models to diagnose and repair run-time property violations in cyber-physical
systems (CPS) with learning-enabled components (LEC). However, given the high
diversity and complexity of LECs, it is challenging to encode domain knowledge
(e.g., the CPS dynamics) in a scalable actual causality model that could
generate useful repair suggestions. In this paper, we focus causal diagnosis on
the input/output behaviors of LECs. Specifically, we aim to identify which
subset of I/O behaviors of the LEC is an actual cause for a property violation.
An important by-product is a counterfactual version of the LEC that repairs the
run-time property by fixing the identified problematic behaviors. Based on this
insights, we design a two-step diagnostic pipeline: (1) construct and
Halpern-Pearl causality model that reflects the dependency of property outcome
on the component's I/O behaviors, and (2) perform a search for an actual cause
and corresponding repair on the model. We prove that our pipeline has the
following guarantee: if an actual cause is found, the system is guaranteed to
be repaired; otherwise, we have high probabilistic confidence that the LEC
under analysis did not cause the property violation. We demonstrate that our
approach successfully repairs learned controllers on a standard OpenAI Gym
benchmark