Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 156-163.
https://doi.org/10.24963/ijcai.2017/23
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. 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 the humans have domain and task models that differ significantly 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 human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
Keywords:
Agent-based and Multi-agent Systems: Agent Theories and Models
Planning and Scheduling: Planning and Scheduling
Multidisciplinary Topics and Applications: Human-Computer Interaction
Robotics and Vision: Human Robot Interaction