Generalizability of goal recognition models in narrative-centered learning environments

Abstract

Recent years have seen growing interest in automated goal recognition. In user-adaptive systems, goal recognition is the problem of recognizing a user’s goals by observing the actions the user performs. Models of goal recognition can support student learning in intelligent tutoring systems, enhance communication efficiency in dialogue systems, or dynamically adapt software to users’ interests. In this paper, we describe an approach to goal recognition that leverages Markov Logic Networks (MLNs)—a machine learning framework that combines probabilistic inference with first-order logical reasoning— to encode relations between problem-solving goals and discovery events, domain- specific representations of user progress in narrative-centered learning environments. We investigate the impact of discovery event representations on goal recognition accuracy and efficiency. We also investigate the generalizability of discovery event-based goal recognition models across two corpora from students interacting with two distinct narrative-centered learning environments. Empirical results indicate that discovery event-based models outperform previous state-of-the-art approaches on both corpora.