Learning with formal representations while preserving correctness guarantees

Identify learning methods that can be applied to formal representational languages in embodied systems while preserving guarantees of correctness needed for safety-critical inference and control.

Background

Formal logical and temporal specifications can offer correctness guarantees that are useful for safety in embodied systems. However, data-driven learning typically provides only statistical guarantees, potentially undermining formal safety assurances.

The authors seek approaches that combine learning with formal languages such that the ability to furnish correctness guarantees is retained.