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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
Modelling Interleaved Activities Using Language Models |
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Authors: |
Eoin Rogers, Robert J.
Ross, John D. Kelleher |
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Published in:
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2020). ECMS 2020 Proceedings
Edited by: Mike Steglich, Christian Muller, Gaby
Neumann, Mathias Walther, European Council for Modeling and Simulation. DOI: http://doi.org/10.7148/2020 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) ISBN: 978-3-937436-68-5 Communications of the ECMS , Volume 34, Issue 1, June 2020, United Kingdom |
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Citation
format: |
Eoin Rogers, Robert J. Ross, John D. Kelleher (2020). Modelling Interleaved Activities Using Language Models, ECMS 2020 Proceedings Edited By: Mike Steglich, Christian Mueller, Gaby Neumann, Mathias
Walther European Council for Modeling and Simulation. doi: 10.7148/2020-0183 |
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DOI: |
https://doi.org/10.7148/2020-0183 |
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Abstract: |
We propose a
new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach
proceeds in multiple stages: we build binary links between activities using probability
distributions generated by a neural language model trained on the dataset,
and combine the binary links to produce complex activities. We then use the
activities as sensor events, allowing us to build complex hierarchies of
activities. We put an emphasis on dealing with interleaving, which represents
a major challenge for many existing activity discovery systems. The system is
tested on a realistic dataset, demonstrating it as a promising solution to
the activity discovery problem. |
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