论文标题
通过偏好推理和解释，一种知识驱动的方法来适应辅助
A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation
论文作者
论文摘要
需要社会辅助机器人（SARS）通过解释其推理来提供其行为的透明度。此外，推理和解释应代表用户的偏好和目标。为了满足对可解释的推理和表示形式的需求，我们建议机器人使用类似的心态来推断用户试图做的事情，并使用提示引擎根据用户试图做的事情找到适当的帮助。如果用户不确定或困惑，机器人为用户提供了解释合成器生成的解释。该解释有助于用户了解机器人对用户偏好的推断以及机器人决定提供其提供帮助的原因。一种知识驱动的方法为推理偏好，帮助和解释提供了透明度，从而促进了用户反馈的合并，并允许机器人学习和适应用户。
There is a need for socially assistive robots (SARs) to provide transparency in their behavior by explaining their reasoning. Additionally, the reasoning and explanation should represent the user's preferences and goals. To work towards satisfying this need for interpretable reasoning and representations, we propose the robot uses Analogical Theory of Mind to infer what the user is trying to do and uses the Hint Engine to find an appropriate assistance based on what the user is trying to do. If the user is unsure or confused, the robot provides the user with an explanation, generated by the Explanation Synthesizer. The explanation helps the user understand what the robot inferred about the user's preferences and why the robot decided to provide the assistance it gave. A knowledge-driven approach provides transparency to reasoning about preferences, assistance, and explanations, thereby facilitating the incorporation of user feedback and allowing the robot to learn and adapt to the user.
