Designing environments conducive to interpretable robot behavior
Anagha Kulkarni, Sarath Sreedharan, et al.
IROS 2020
Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where users have domain and task models that differ from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a “model reconciliation problem” (MRP), where the AI system in effect suggests changes to the user's mental model so as to make its plan be optimal with respect to that changed user model. We will study the properties of such explanations, present algorithms for automatically computing them, discuss relevant extensions to the basic framework, and evaluate the performance of the proposed algorithms both empirically and through controlled user studies.
Anagha Kulkarni, Sarath Sreedharan, et al.
IROS 2020
Junkyu Lee, Michael Katz, et al.
NeurIPS 2023
Emre Goynugur, Kartik Talamadupula, et al.
ICAPS 2016
Shirin Sohrabi, Anton Riabov, et al.
IJCAI 2016