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.

翻译：智能对话代理的下一步发展是摆脱其作为沉默旁观者的角色，转变为主动参与者。明确界定的主动行为可能改善人机协作，因为代理在交互过程中承担更积极的角色，并减轻用户的负担。然而，主动性是一把双刃剑，因为执行不当的预判性行为不仅会对任务结果产生破坏性影响，还会损害与用户的关系。为了设计合适的主动对话策略，我们提出了一种新颖的方法，在对话中同时融入社交特征和任务相关特征。在此，主要目标是优化主动行为，使其既以任务为导向（这要求任务成功率和效率较高），又通过增强用户信任来提升社交效果。通过将这两方面纳入强化学习训练主动对话代理的奖励函数中，我们的方法在人机协作方面展现出更成功的优势。