Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2590-2595.
Abstract
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.
@inproceedings{ijcai2016p368,
title = {Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks},
author = {Min, Wookhee and Mott, Bradford W. and Rowe, Jonathan P. and Liu, Barry and Lester, James C.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2590--2595},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/368},
}
TY - CPAPER TI - Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks AU - Min, Wookhee AU - Mott, Bradford W. AU - Rowe, Jonathan P. AU - Liu, Barry AU - Lester, James C. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2590 EP - 2595 UR - https://ijcai.org/proceedings/2016/368 ER -Copied
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