Name
Abstract | ||
|---|---|---|
The evolution of ambient intelligence systems has allowed for the development of adaptable systems. These systems trace user's habits in an automatic way and act accordingly, resulting in a context aware system. The goal is to make these systems adaptable to the user's environment, without the need for their direct interaction. This paper proposes a system that can learn from users' behavior. In order for the system to perform effectively, an adaptable multi agent system is proposed and the results are compared with the use of several classifiers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SSCI.2016.7849857 | 2016 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | Field | DocType |
classifiers,multi-agent systems,ambient intelligence | Ambient intelligence,Computer science,Multi-agent system,Human–computer interaction,Artificial intelligence,User modeling,Artificial neural network,Statistical classification,Hybrid system,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-4241-8 | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valérian Guivarch | 1 | 9 | 2.67 |
| Juan Francisco de Paz | 2 | 395 | 52.24 |
| Gabriel Villarrubia | 3 | 183 | 24.85 |
| Javier Bajo | 4 | 1451 | 118.96 |
| André Péninou | 5 | 77 | 22.78 |
| Valérie Camps | 6 | 90 | 17.42 |