Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006254103080317 | ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2 |
Keywords | Field | DocType |
Dempster-Shafer Theory,Event Detection,Event Inference,Uncertain Event-observations | Data mining,Computer science,Inference,Artificial intelligence,Dempster–Shafer theory,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Calderwood | 1 | 2 | 1.39 |
| Kevin McAreavey | 2 | 23 | 8.16 |
| Weiru Liu | 3 | 1597 | 112.05 |
| Jun Hong | 4 | 49 | 8.74 |