Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks / 2590
Wookhee Min, Bradford Mott, Jonathan Rowe, Barry Liu, James Lester

Recent years have seen a growing interest in player modeling for digital games. Goal recognition, which aims to accurately recognize players' goals from observations of low-level player actions, is a key problem in player modeling. However, player goal recognition poses significant challenges because of the inherent complexity and uncertainty pervading gameplay. In this paper, we formulate player goal recognition as a sequence labeling task and introduce a goal recognition framework based on long short-term memory (LSTM) networks. Results show that LSTM-based goal recognition is significantly more accurate than previous state-of-the-art methods, including n-gram encoded feedforward neural networks pre-trained with stacked denoising autoencoders, as well as Markov logic network-based models. Because of increased goal recognition accuracy and the elimination of labor-intensive feature engineering, LSTM-based goal recognition provides an effective solution to a central problem in player modeling for open-world digital games.

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