{"type":"ScholarlyArticle","title":"Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks","authors":[{"name":"Wookhee Min","slug":"wookhee-min","given":"Wookhee","family":"Min","affiliation":""},{"name":"Bradford W. Mott","slug":"bradford-w-mott","given":"Bradford W.","family":"Mott","affiliation":""},{"name":"Jonathan P. Rowe","slug":"jonathan-p-rowe","given":"Jonathan P.","family":"Rowe","affiliation":""},{"name":"Barry Liu","slug":"barry-liu","given":"Barry","family":"Liu","affiliation":""},{"name":"James C. Lester","slug":"james-c-lester","given":"James C.","family":"Lester","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/368","pdf":"https://ijcai.org/proceedings/2016/0368.pdf","markdown":"https://ijcai.org/proceedings/2016/368.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/368","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":368,"pages":{"first":2590,"last":2595},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}