A logic-based explanation generation framework for classical and hybrid planning problems (extended abstract)

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation , has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning , and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.