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.
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
