A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)

Stylianos Loukas Vasileiou, William Yeoh, Son Tran, Ashwin Kumar, Michael Cashmore, Daniele Magazzeni

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.