The International Joint Conference on Artificial Intelligence (IJCAI) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, during the conference opening ceremony, three articles were named as distinguished papers. Abstract: Normative Restraining Bolts (NRBs) adapt the restraining bolt technique (originally developed for safe reinforcement learning) to ensure compliance with social, legal, and ethical norms. While effective, NRBs rely on trial-and-error weight tuning, which hinders their ability to enforce hierarchical norms; moreover, norm updates require retraining. In this paper, we reformulate learning with NRBs as a multi-objective reinforcement learning (MORL) problem, where each norm is treated as a distinct objective.

The International Joint Conference on Artificial Intelligence (IJCAI) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, during the conference opening ceremony, three articles were named as distinguished papers. Abstract: As digital marketplaces and services continue to expand, it is crucial to maintain a safe and fair environment for all users. This requires implementing fairness constraints into the sequential decision-making processes of these platforms to ensure equal treatment. However, this can be challenging as these processes often need to solve NP-complete problems with exponentially large decision spaces at each time step.