Proceedings

ASP-Based Probabilistic Policy Fixing for Norm Compliant RL

Sebastian Adam, Thomas Eiter

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 3765-3773.

https://doi.org/10.24963/ijcai.2026/419

Keywords

  • Knowledge Representation and Reasoning: Reasoning about actions
  • Knowledge Representation and Reasoning: Logic programming

Abstract

Reinforcement learning (RL) is commonly used to learn reward-optimizing policies. However, RL policies are not always trained with ethical behavior in mind, which can lead an agent to violate social or legal norms in pursuit of its goal. Retraining agents with additional norms is not always feasible, especially in complex stochastic environments. To mitigate this issue, we present a probabilistic policy fixing framework that adapts norm-agnostic policies online. Using Answer Set Programming (ASP), we generate policy fixes that minimize deviations from the RL policy while optimizing for norm adherence against a set of sampled worlds. Based on the Rule of Three and Hoeffding's inequality, we provide guarantees that fixed policies are near optimal, given a specified level of confidence.

Sebastian Adam, and Thomas Eiter. 2026. ASP-Based Probabilistic Policy Fixing for Norm Compliant RL. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 3765–3773. https://doi.org/10.24963/ijcai.2026/419
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