Deep LSTM-Based Goal Recognition Models for Open-World Digital Games

Player goal recognition in digital games offers the promise of enabling games to dynamically customize player experience. Goal recognition aims to recognize players’ high-level intentions using a computational model trained on a player behavior corpus. A significant challenge is posed by devising reliable goal recognition models with a behavior corpus characterized by highly idiosyncratic player actions. In this paper, we introduce deep LSTM-based goal recognition models that handle the inherent uncertainty stemming from noisy, non-optimal player behaviors. Empirical evaluation indicates that deep LSTMs outperform competitive baselines including single-layer LSTMs, n-gram encoded feedforward neural networks, and Markov logic networks for a goal recognition corpus collected from an open-world educational game. In addition to metric-based goal recognition model evaluation, we investigate a visualization technique to show a dynamic goal recognition model’s performance over the course of a player’s goal-seeking behavior. Deep LSTMs, which are capable of both sequentially and hierarchically extracting salient features of player behaviors, show significant promise as a goal recognition approach for open-world digital games.