Towards empathic autonomous agents

Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We work towards a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) byl earning their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable.To provide a high-level overview of our work, we propose a reasoning-loop architecture to address the problem in focus. To realize specific components of the architecture, we suggest applying existing concepts in argumentation and utility theory. Reinforcement learning methods can be used by the agent to learn from and interact with its environment