Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
Yinxu Tang, Stylianos Loukas Vasileiou, Vincent Derkinderen, William Yeoh
In human-AI interaction, effective communication relies on aligning the AI agent’s model with the human user’s mental model -- a process known as model reconciliation. However, existing model reconciliation approaches predominantly assume deterministic models, overlooking the fact that human knowledge is often uncertain or probabilistic. To bridge this gap, we present a probabilistic model reconciliation framework that resolves inconsistencies in MPE outcome probabilities between an agent’s and a user’s models.Our approach is built on probabilistic logic programming (PLP) using ProbLog, where explanations are generated as cost-optimal model updates that reconcile these probabilistic differences. We develop two search algorithms -- a generic and an optimized version, with the latter guided by theoretical insights for scalability. Our approach is validated through a user study on how explanation types impact user understanding and computational experiments showing the optimized search consistently outperforms the generic.