On the Computational Complexity of Model Reconciliations
Abstract
Model-reconciliation explanation is a popular framework for generating explanations for planning problems. While the framework has been extended to multiple settings since its introduction for classical planning problems, there is little agreement on the computational complexity of generating minimal model reconciliation explanations in the basic setting. In this paper, we address this lacuna by introducing a decision-version of the model-reconciliation explanation generation problem and we show that it is Sigma-2-P Complete.
Cite
Text
Sreedharan et al. "On the Computational Complexity of Model Reconciliations." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/646Markdown
[Sreedharan et al. "On the Computational Complexity of Model Reconciliations." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/sreedharan2022ijcai-computational/) doi:10.24963/IJCAI.2022/646BibTeX
@inproceedings{sreedharan2022ijcai-computational,
title = {{On the Computational Complexity of Model Reconciliations}},
author = {Sreedharan, Sarath and Bercher, Pascal and Kambhampati, Subbarao},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4657-4664},
doi = {10.24963/IJCAI.2022/646},
url = {https://mlanthology.org/ijcai/2022/sreedharan2022ijcai-computational/}
}