Generation and evaluation of adaptive explanations based on dynamic partner-modeling and non-stationary decision making

Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialog systems. We adopted the approach of treating explanation generation as a non-stationary decision process, in which the optimal strategy varies with changing beliefs about the explainee and the interaction context. In this study, we addressed the questions of (1) how to track the interaction context and the relevant adaptation parameters in a formally defined computational partner model (PM), and (2) how to utilize this model in the dynamically adjusted, rational decision process that determines the currently best explanation strategy. We proposed a Bayesian inference-based approach to continuously update the PM based on user feedback, and a non-stationary Markov Decision Process to adjust decision-making based on the PM values. We evaluated an implementation of this framework in an online user study, showing the positive effects of a broader PM. The results showed that an adapted explanation leads to a higher level of user understanding, highlighting the potential of our approach to improve current dialog systems.