Name
Abstract | ||
|---|---|---|
In this paper, we optimize the predictive performance of a Situation-Aware FEar Learning model (SAFEL) by investigating the relationship between its parameters. SAFEL is a hybrid computational model based on the fear-learning system of the brain, which was developed to provide robots with the capability to predict threatening or undesirable situations based on temporal context. The main aim of this work is to improve SAFEL's emotional response. An emotional response coherent with environmental changes is essential not only for self-preservation and adaptation purposes, but also for improving the believability and interaction skills of companion robots. Experiments with a NAO humanoid robot show that adjusting the ratio between two parameters of SAFEL can significantly increase the predictive performance and reduce parameter settings. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ROMAN.2016.7745201 | 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) |
Keywords | Field | DocType |
predictive performance,SAFEL,situation-aware fear learning model,hybrid computational model,brain,temporal context,emotional response,environmental changes,self-preservation purpose,adaptation purpose,companion robots,NAO humanoid robot | Simulation,Computer science,Context model,Artificial intelligence,Temporal context,Robot,Humanoid robot | Conference |
ISSN | ISBN | Citations |
1944-9445 | 978-1-5090-3930-2 | 1 |
PageRank | References | Authors |
0.36 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caroline Rizzi Raymundo | 1 | 13 | 2.00 |
| Colin G. Johnson | 2 | 933 | 115.57 |
| Patrícia Amâncio Vargas | 3 | 93 | 12.13 |