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.

通过采用强化学习算法，研究设计基于社交和任务相关特征的行为预测策略是智能对话代理下一步的发展，以期通过优化设定的预测性对话策略，增强用户满意度和任务效率，并在实践操作中取得较好的协同效果。