Name
Abstract | ||
|---|---|---|
Ubiquitous computing aims for the realization of environments that assist users autonomously and proactively. Therefore smart environment infrastructures need to be able to identify users needs (intention recognition) and to plan an appropriate assisting strategy. Both is matter for research. In our approach we address inferring the intention of a team within a smart meeting environment. This becomes a central challenge, especially if multiple users are observed by noisy heterogeneous sensors. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/COLCOM.2007.4553875 | CollaborateCom |
Keywords | Field | DocType |
smart meeting environment,multiple user,central challenge,users need,users autonomously,intention recognition,ubiquitous computing,noisy heterogeneous sensor,smart environment infrastructure,model-based intention inference,cooperative team,history,bayesian methods,sensors,particle filters,probability distribution,smart environment,particle filter,behavior modeling,filtering,dynamic bayesian network,hidden markov models | Smart environment,Computer science,Inference,Particle filter,Human–computer interaction,Artificial intelligence,Ubiquitous computing,Hidden Markov model,Machine learning,Bayesian probability,Distributed computing,Dynamic Bayesian network | Conference |
Citations | PageRank | References |
4 | 0.43 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Giersich | 1 | 44 | 5.07 |
| Thomas Kirste | 2 | 93 | 18.37 |