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Modelling and reasoning with uncertain event-observations for event inference

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Modelling and reasoning with uncertain event-observations for event inference. / Calderwood, Sarah; McAreavey, Kevin; Liu, Weiru; Hong, Jun.

Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17): February 24-26, 2017, in Porto, Portugal. ed. / Jaap van den Herik; Ana Paula Rocha; Joaquim Filipe. Vol. II SciTePress, 2017. p. 308-317.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Calderwood, S, McAreavey, K, Liu, W & Hong, J 2017, Modelling and reasoning with uncertain event-observations for event inference. in J van den Herik, AP Rocha & J Filipe (eds), Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17): February 24-26, 2017, in Porto, Portugal. vol. II, SciTePress, pp. 308-317. DOI: 10.5220/0006254103080317

APA

Calderwood, S., McAreavey, K., Liu, W., & Hong, J. (2017). Modelling and reasoning with uncertain event-observations for event inference. In J. van den Herik, A. P. Rocha, & J. Filipe (Eds.), Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17): February 24-26, 2017, in Porto, Portugal. (Vol. II, pp. 308-317). SciTePress. DOI: 10.5220/0006254103080317

Vancouver

Calderwood S, McAreavey K, Liu W, Hong J. Modelling and reasoning with uncertain event-observations for event inference. In van den Herik J, Rocha AP, Filipe J, editors, Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17): February 24-26, 2017, in Porto, Portugal. Vol. II. SciTePress. 2017. p. 308-317. Available from, DOI: 10.5220/0006254103080317

Author

Calderwood, Sarah; McAreavey, Kevin; Liu, Weiru; Hong, Jun / Modelling and reasoning with uncertain event-observations for event inference.

Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17): February 24-26, 2017, in Porto, Portugal. ed. / Jaap van den Herik; Ana Paula Rocha; Joaquim Filipe. Vol. II SciTePress, 2017. p. 308-317.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bibtex

@inbook{98464acbe9e642ed9c57e44d1267f350,
title = "Modelling and reasoning with uncertain event-observations for event inference",
keywords = "Dempster-Shafer theory, Event detection, Event inference, Uncertain event-observations",
author = "Sarah Calderwood and Kevin McAreavey and Weiru Liu and Jun Hong",
year = "2017",
month = "4",
doi = "10.5220/0006254103080317",
isbn = "9789897582202",
volume = "II",
pages = "308--317",
editor = "{van den Herik}, Jaap and Rocha, {Ana Paula} and Joaquim Filipe",
booktitle = "Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17)",
publisher = "SciTePress",

}

RIS - suitable for import to EndNote

TY - CHAP

T1 - Modelling and reasoning with uncertain event-observations for event inference

AU - Calderwood,Sarah

AU - McAreavey,Kevin

AU - Liu,Weiru

AU - Hong,Jun

PY - 2017/4/27

Y1 - 2017/4/27

N2 - This paper presents an event modelling and reasoning framework where event-observations obtained from heterogeneous sources may be uncertain or incomplete, while sensors may be unreliable or in conflict. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the event-observations so that they can be combined in a consistent way. Unfortunately, existing frameworks do not specify which event-observations should be selected to combine. Our framework provides a rule-based approach to ensure combination occurs on event-observations from multiple sources corresponding to the same event of an individual subject. In addition, our framework provides an inference rule set to infer higher level inferred events by reasoning over the uncertain event-observations as epistemic states using a formal language. Finally, we illustrate the usefulness of the framework using a sensor-based surveillance scenario.

AB - This paper presents an event modelling and reasoning framework where event-observations obtained from heterogeneous sources may be uncertain or incomplete, while sensors may be unreliable or in conflict. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the event-observations so that they can be combined in a consistent way. Unfortunately, existing frameworks do not specify which event-observations should be selected to combine. Our framework provides a rule-based approach to ensure combination occurs on event-observations from multiple sources corresponding to the same event of an individual subject. In addition, our framework provides an inference rule set to infer higher level inferred events by reasoning over the uncertain event-observations as epistemic states using a formal language. Finally, we illustrate the usefulness of the framework using a sensor-based surveillance scenario.

KW - Dempster-Shafer theory

KW - Event detection

KW - Event inference

KW - Uncertain event-observations

U2 - 10.5220/0006254103080317

DO - 10.5220/0006254103080317

M3 - Conference contribution

SN - 9789897582202

VL - II

SP - 308

EP - 317

BT - Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17)

PB - SciTePress

ER -