HRI: Learning Mixed-Initiative Dialogue Strategies

该研究项目使下一代对话系统能够与用户协作，而不受系统主动交互的限制，以便以最佳方式解决复杂的任务。该研究开发了强化学习(RL)策略，以学习混合倡议的对话策略。这样做的具体目的是(A)将RL扩展到混合主动对话交互；(B)允许系统策略适应不同类型的用户，例如记忆力较差的人或解决问题的技能较差的人；以及(C)同时学习针对模拟用户的策略。这种方法将允许部署更先进的对话系统，例如帮助老年人使他们能够更长时间地独立生活，并帮助向农村地区提供医疗保健信息。拟议的研究项目将产生一个工具包，使广泛的用户能够轻松地制定对话政策。该工具包将(A)让学生在这一领域得到有效的培训，(B)降低其他研究人员为该领域做出贡献的门槛，以及(C)帮助将这项新技术转移到工业中。

This research project enables next generation dialogue systems to be able to collaborate with a user without the limitations of system-initiative interaction, in order to solve complex tasks in an optimal manner. The research develops reinforcement learning (RL) strategies to learn dialogue policies that are mixed-initiative. The specific aims of this are to (a) extend RL to mixed-initiative dialogue interaction; (b) allow the system policy to adapt to different user types, such as people with poor memory, or poor problem-solving skills; and (c) simultaneously learn the policy for the simulated user. This approach will allow more advanced dialogue systems to be deployed, such as assisting the elderly so they can live independently longer, and helping provide health care information to rural areas. The proposed research project will result in a toolkit that will allow a wide range of users to easily develop dialogue policies. The toolkit will (a) allow students to be effectively trained in this area, (b) lower the barrier for other researchers to contribute to the field, and (c) help transfer this new technology to industry.