Name
Abstract | ||
|---|---|---|
We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction. This is the first extension of the event calculus capable of handling numerous sources of uncertainty (e.g. from primitive event observations and from composite event definitions). It is also the first extension capable of handling multiple sources of event observations (e.g. in multi-sensor environments). We describe characteristics of this new extension (e.g. rationality of conclusions), and prove some important properties (e.g. validity in ProbLog). Our extension is directly implementable in ProbLog, and we successfully apply it to the problem of activity recognition under uncertainty in an event detection data set obtained from vision analytics of bus surveillance video. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5555/3091125.3091146 | AAMAS |
Keywords | Field | DocType |
The event calculus,event reasoning,probabilistic logic programming,ProbLog,annotated disjunction | Probabilistic logic programming,Event calculus,Event tree,Rationality,Activity recognition,Computer science,Language construct,Theoretical computer science,Artificial intelligence,Probabilistic logic,Analytics,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin McAreavey | 1 | 23 | 8.16 |
| Kim Bauters | 2 | 38 | 7.91 |
| Weiru Liu | 3 | 1597 | 112.05 |
| Jun Hong | 4 | 49 | 8.74 |