Abstract
The manner in which the human behaviour and the environment are modelled greatly influences the activity recognition performance in context-aware systems and an inappropriate choice of modelling mechanism could lead to unwanted or unexpected model behaviour. In this work we present an approach for modelling human behaviour based on precondition-effect rules, and discuss in detail different modelling strategies. As a result, the paper provides useful guidelines for modelling human behaviour for activity recognition, including best practices and pitfalls that should be avoided for one to build a successful model.
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Yordanova, K., Krüger, F., Kirste, T. (2012). Strategies for Modelling Human Behaviour for Activity Recognition with Precondition-Effect Rules. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_27
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DOI: https://doi.org/10.1007/978-3-642-33347-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33346-0
Online ISBN: 978-3-642-33347-7
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