Logical Inference Under Explicit Constraint Boundaries in Hybrid Neuro-Symbolic Systems

Abstract

Hybrid neuro-symbolic reasoning introduces the ability to combine adaptive neural policy learning with explicit logical constraints required in industrial robotic environments. This article presents a constraint-bounded inference framework that positions a symbolic rule layer between neural action proposals and the robotic actuation pipeline, ensuring that decision-making remains interpretable, safe, and operationally feasible during continuous production workflows. The results show that the system maintains task continuity under constraint pressure, prevents unsafe behavior in dynamic or partially observable conditions, and enables rapid updates to operational rules without retraining neural models. The architecture also improves transparency by producing human-readable rationale for action arbitration, supporting industrial audit and supervisory requirements. The findings demonstrate that constraint-bounded logical inference provides a scalable foundation for safe and reliable autonomous robotics in dynamic manufacturing scenarios.