2026
On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios (Abstract Reprint)
AAAI 2026technical
Explanation generation frameworks aim to make AI systems’ decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a