Rules2Lab: from Prolog Knowledge-Base, to Learning Agents, to Norm Engineering

This paper proposes a methodology, called Rules2Lab, that maps a Prolog knowledge base onto a Gymnasium environment. States, actions, and constraints are defined in Prolog, while temporal, computational, and sub-symbolic operations are delegated to Python. We demonstrate our approach through a case study on privacy vulnerabilities in a data marketplace. In our simulation, a reinforcement learning agent attempts to access sensitive data, with a privacy breach defined by the similarity between inferred and private data. Inductive logic programming is then used to engineer a new norm that prevents such breaches, demonstrating how a new rule can be seamlessly integrated into the knowledge base. Preliminary results highlight how a Gymnasium environment can be effectively combined with logic-based modeling and inference.