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There is a need for socially assistive robots (SARs) to provide transparencyin their behavior by explaining their reasoning. Additionally, the reasoningand explanation should represent the user's preferences and goals. To worktowards satisfying this need for interpretable reasoning and representations,we propose the robot uses Analogical Theory of Mind to infer what the user istrying to do and uses the Hint Engine to find an appropriate assistance basedon what the user is trying to do. If the user is unsure or confused, the robotprovides the user with an explanation, generated by the ExplanationSynthesizer. The explanation helps the user understand what the robot inferredabout the user's preferences and why the robot decided to provide theassistance it gave. A knowledge-driven approach provides transparency toreasoning about preferences, assistance, and explanations, thereby facilitatingthe incorporation of user feedback and allowing the robot to learn and adapt tothe user.

A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation