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

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

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Journal Track. Pages 6985-6989. https://doi.org/10.24963/ijcai.2023/795

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.
Keywords:
Planning and Scheduling: PS: Model-based reasoning
Humans and AI: HAI: Human-AI collaboration
Knowledge Representation and Reasoning: KRR: Diagnosis and abductive reasoning
Knowledge Representation and Reasoning: KRR: Belief change