From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems

Explain Like I'm Normal

Researchers have developed a way to turn 'black box' reinforcement learning models into human-readable Prolog code. By distilling complex neural policies into logical rules, they've created a system that can be audited by humans and refined by optimizers for better performance. This bridges the gap between raw neural power and transparent, symbolic logic.