Argumentative Reward Learning: Reasoning About Human Preferences

We reward learning, which combines use PBA to represent and reason non-monotonically about preference-based argumentation with existing approaches human preferences, allowing the agent to draw conclusions to reinforcement learning from human defeasibly, with the ability to retract these conclusions under feedback. Our method improves prior work by the light of further interaction with the human.