Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
This addresses the need for user-specific explanations in autonomous systems, but it is incremental as it builds on existing explanation generation methods.
The paper tackles the problem of explaining robot plans to users with varying expertise levels by representing the user's model as an abstraction of the planner's domain model, and it shows that the approach can efficiently compute explanations for various problems.
There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users. One aspect of the explanation generation problem that has yet to receive much attention is the task of explaining plans to users whose level of expertise differ from that of the explainer. We propose an approach for addressing this problem by representing the user's model as an abstraction of the domain model that the planner uses. We present algorithms for generating minimal explanations in cases where this abstract human model is not known. We reduce the problem of generating explanation to a search over the space of abstract models and investigate possible greedy approximations for minimal explanations. We also empirically show that our approach can efficiently compute explanations for a variety of problems.