Modelling and reasoning with uncertain event-observations for event inference

Sarah Calderwood, Kevin McAreavey, Weiru Liu, Jun Hong

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
267 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART'17)
Subtitle of host publicationFebruary 24-26, 2017, in Porto, Portugal
EditorsJaap van den Herik, Ana Paula Rocha, Joaquim Filipe
Number of pages10
ISBN (Print)9789897582202
Publication statusPublished - 27 Apr 2017

Structured keywords

  • Jean Golding


  • Dempster-Shafer theory
  • Event detection
  • Event inference
  • Uncertain event-observations


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