@inproceedings{10.5555/3545946.3598956,
  author = {Alcaraz, Beno\^{\i}t and Boissier, Olivier and Chaput, R\'{e}my and Leturc, Christopher},
  title = {AJAR: An Argumentation-Based Judging Agents Framework for Ethical Reinforcement Learning},
  year = {2023},
  isbn = {9781450394321},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  address = {Richland, SC},
  abstract = {An increasing number of socio-technical systems embedding Artificial Intelligence (AI) technologies are deployed, and questions arise about the possible impact of such systems onto humans. We propose a hybrid multi-agent Reinforcement Learning framework consists of learning agents that learn a task-oriented behaviour defined by a set of symbolic moral judging agents to ensure they respect moral values. This framework is applied on the problem of responsible energy distribution for smart grids.},
  booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
  pages = {2427–2429},
  numpages = {3},
  keywords = {argumentation, hybrid neural-symbolic learning, machine ethics, ethical judgment, artificial moral agent, reinforcement learning},
  location = {London, United Kingdom},
  series = {AAMAS '23}
}