Model-Free Model Reconciliation
This work addresses the challenge of generating explanations in automated decision-making for cases where user knowledge is not declaratively provided, representing an incremental advancement in explanation methods.
The paper tackles the problem of explaining complex sequential decisions when user models are not explicitly available, by adapting the model reconciliation approach to propose a simple labeling model that helps identify information to reconcile user and agent models.
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.