The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.
@article{arxiv.2211.15359,
title = {Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning},
author = {Matthias Kraus and Nicolas Wagner and Ron Riekenbrauck and Wolfgang Minker},
journal= {arXiv preprint arXiv:2211.15359},
year = {2023}
}