Multimodal goal recognition in open-world digital games

Abstract

Recent years have seen a growing interest in player modeling to create player-adaptive digital games. As a core player-modeling task, goal recognition aims to recognize players’ latent, high-level intentions in a non-invasive fashion to deliver goal-driven, tailored game experiences. This paper reports on an investigation of multimodal data streams that provide rich evidence about players’ goals. Two data streams, game event traces and player gaze traces, are utilized to devise goal recognition models from a corpus collected from an open-world serious game for science education. Empirical evaluations of 140 players’ trace data suggest that multimodal LSTM-based goal recognition models outperform competitive baselines, including unimodal LSTMs as well as multimodal and unimodal CRFs, with respect to predictive accuracy and early prediction. The results demonstrate that player gaze traces have the potential to significantly enhance goal recognition models’ performance.