  Being able to recognize emotions in human users is considered a highly
desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
contemporary approaches rarely attempt to apply recognized emotional features
in an active manner to modulate robot decision-making and dialogue for the
benefit of the user. In this position paper, we propose a method of
incorporating recognized emotions into a Reinforcement Learning (RL) based
dialogue management module that adapts its dialogue responses in order to
attempt to make cognitive training tasks, like the 2048 Puzzle Game, more
enjoyable for the users.
