# Model-Free Model Reconciliation

arXiv: https://arxiv.org/abs/1903.07198 · HTML: https://mlsanity.com/paper/1903.07198/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Not all users are the same: Providing personalized explanations for sequential decision making problems](https://mlsanity.com/paper/2106.12207/) (2021)
- [Differential Assessment of Black-Box AI Agents](https://mlsanity.com/paper/2203.13236/) (2022)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?](https://mlsanity.com/paper/2208.03091/) (2022)
- [Towards Reconciling Usability and Usefulness of Explainable AI Methodologies](https://mlsanity.com/paper/2301.05347/) (2023)
- [Explanation through Reward Model Reconciliation using POMDP Tree Search](https://mlsanity.com/paper/2305.00931/) (2023)
- [Reducing Human-Robot Goal State Divergence with Environment Design](https://mlsanity.com/paper/2404.15184/) (2024)
- [U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning](https://mlsanity.com/paper/2605.02765/) (2026)

## Abstract (arXiv)

Designing agents capable of explaining complex sequential decisions remain a significant open problem in automated decision-making. Recently, there has been a lot of interest in developing approaches for generating such explanations for various decision-making paradigms. One such approach has been the idea of em explanation as model-reconciliation. The framework hypothesizes that one of the common reasons for the user's confusion could be the mismatch between the user's model of the task and the one used by the system to generate the decisions. While this is a general framework, most works that have been explicitly built on this explanatory philosophy have focused on settings where the model of user's knowledge is available in a declarative form. Our goal in this paper is to adapt the model reconciliation approach to the cases where such user models are no longer explicitly provided. We present a simple and easy to learn labeling model that can help an explainer decide what information could help achieve model reconciliation between the user and the agent.