paper-conference

Reinforcement learning (RL) is becoming more prevalent in practical domains with human implications, raising ethical questions. …

This paper presents a novel Multi-Objective Reinforcement Learning approach to settle dilemmas, by putting humans in the loop.

Paper presented at the Autonomous Agents and Multiagent Systems conference. It presents the AJAR framework, which uses argumentation-based judging agents to provide rewards for Reinforcement Learning agents, according to one or several moral values. This “judgment of ethics” is used to nudge the learning agents towards an “ethical behavior”, that is, a behavior aligned with the given moral values.

This paper presents how Artificial Intelligence could be used to help humans in their ethical decision-making tasks.

Paper on the ability to use a symbolic reasoning approach to judge neural learning agents, in order to reward them appropriately with respect to their ’ethical’ behavior, combining both approaches in a Hybrid method.

Paper presented at the AI, Ethics, and Society conference. It presents a novel, hybrid, method to learn “ethical behaviors” by combining symbolic judgments with a Reinforcement Learning algorithm.

Paper on adaptive learning of ethical behaviors I presented at JFSMA (French Days of Multi-Agent Systems).