@inproceedings{71ec1a7028564e599b4652b13b8c2c67,
title = "A logic-based explanation generation framework for classical and hybrid planning problems (extended abstract)",
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 problems, model reconciliation, human user",
author = "Vasileiou, {Stylianos Loukas} and William Yeoh and Son Tran and Ashwin Kumar and Michael Cashmore and Daniele Magazzeni",
year = "2023",
month = aug,
doi = "10.24963/ijcai.2023/795",
language = "English",
series = "Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence Organization",
pages = "6985--6989",
editor = "Edith Elkind",
booktitle = "Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence",
note = "Thirty-Second International Joint Conference on Artificial Intelligence ; Conference date: 19-08-2023 Through 25-08-2023",
}