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.
Keywords
- Planning and Scheduling: Theoretical Foundations of Planning
- AI Ethics, Trust, Fairness: Explainability and Interpretability
