# Not all users are the same: Providing personalized explanations for sequential decision making problems

arXiv: https://arxiv.org/abs/2106.12207 · HTML: https://mlsanity.com/paper/2106.12207/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Efficient Model Learning for Human-Robot Collaborative Tasks](https://mlsanity.com/paper/1405.6341/) (2014)
- [Model-Free Model Reconciliation](https://mlsanity.com/paper/1903.07198/) (2019)
- [Online Explanation Generation for Human-Robot Teaming](https://mlsanity.com/paper/1903.06418/) (2019)

## Built on by (most cited first, 7 of 7)

- [Explaining Preference-driven Schedules: the EXPRES Framework](https://mlsanity.com/paper/2203.08895/) (2022)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [Reducing Human-Robot Goal State Divergence with Environment Design](https://mlsanity.com/paper/2404.15184/) (2024)
- [Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies](https://mlsanity.com/paper/2403.19760/) (2024)
- [Explainable Human-AI Interaction: A Planning Perspective](https://mlsanity.com/paper/2405.15804/) (2024)
- [Personalised Explanations in Long-term Human-Robot Interactions](https://mlsanity.com/paper/2507.03049/) (2025)
- [Adaptive XAI in High Stakes Environments: Modeling Swift Trust with Multimodal Feedback in Human AI Teams](https://mlsanity.com/paper/2507.21158/) (2025)

## Abstract (arXiv)

There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the user-model are completely ignored. The handful of works that look at tailoring their explanation to the user's background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is thus to propose an end-to-end adaptive explanation generation system that begins by learning the different types of users that the agent could interact with. Then during the interaction with the target user, it is tasked with identifying the type on the fly and adjust its explanations accordingly. The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP. We demonstrate the usefulness of our system on two domains using state-of-the-art POMDP solvers. We also report the results of a user study that investigates the benefits of providing personalized explanations in a human-robot interaction setting.