@inproceedings{38c079ae96ab4154ad22d7ba7db0978f,
title = "Recognizing player's activities and hidden state",
abstract = "This paper describes a machine learning approach to classifying the activities of players in games. Instances of activities generally are not identical because they play out in different contexts, so the challenge is to extract the {"}essences{"} of activities from instances. We show how this problem may be mapped to a sequence alignment problem, for which there are polynomial-time solutions. The method works well even when some features of activities are not observable (e.g., the emotional states of players). In fact, these features can in some conditions be inferred with high accuracy.",
keywords = "Activity classification, Hidden state, Machine learning",
author = "Wesley Kerr and Cohen, {Paul R.} and Niall Adams",
year = "2011",
doi = "10.1145/2159365.2159377",
language = "English (US)",
isbn = "9781450308045",
series = "Proceedings of the 6th International Conference on the Foundations of Digital Games, FDG 2011",
pages = "84--90",
booktitle = "Proceedings of the 6th International Conference on the Foundations of Digital Games, FDG 2011",
note = "6th International Conference on the Foundations of Digital Games, FDG 2011 ; Conference date: 29-06-2011 Through 01-07-2011",
}