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

arXiv: https://arxiv.org/abs/2012.02904 · HTML: https://mlsanity.com/paper/2012.02904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Enabling a Social Robot to Process Social Cues to Detect when to Help a User](https://mlsanity.com/paper/2110.11075/) (2021)
- [AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI](https://mlsanity.com/paper/2210.11832/) (2022)
- [Human Autonomy as a Design Principle for Socially Assistive Robots](https://mlsanity.com/paper/2211.06748/) (2022)

## Abstract (arXiv)

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.