Name
Abstract | ||
|---|---|---|
Recently, in the field of activity recognition a number of approaches that utilise probabilistic symbolic models have been proposed. Such approaches rely on the combination of symbolic state-space models and probabilistic inference techniques in order to recognise the user activities in situations with uncertainty. One problem with such approaches is the huge state space that can be generated just by a few rules. In this work we investigate the effects of a mechanism for reducing the model complexity on symbolic level. To illustrate the approach, we present one possible strategy and discuss its effects on the model size and the probability of selecting the correct action in an office scenario. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10554-3_31 | ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, AND APPLICATIONS |
Keywords | DocType | Volume |
symbolic human behaviour models, context awareness, activity recognition | Conference | 8722 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.38 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kristina Yordanova | 1 | 70 | 15.22 |
| Martin Nyolt | 2 | 15 | 2.46 |
| Thomas Kirste | 3 | 93 | 18.37 |