Skip to main content

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

Rizzi, Caroline, Johnson, Colin G., Vargas, Patricia A. (2016) Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model. In: 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). . pp. 736-742. IEEE ISBN 978-1-5090-3930-2. E-ISBN 978-1-5090-3929-6. (doi:10.1109/ROMAN.2016.7745201)

PDF - Author's Accepted Manuscript
Download (226kB) Preview
[img]
Preview
PDF - Author's Accepted Manuscript
Restricted to Repository staff only
Contact us about this Publication Download (215kB)
[img]
Official URL
http://dx.doi.org/10.1109/ROMAN.2016.7745201

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ROMAN.2016.7745201
Divisions: Faculties > Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 17 Feb 2017 11:36 UTC
Last Modified: 29 May 2019 18:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60437 (The current URI for this page, for reference purposes)
Rizzi, Caroline: https://orcid.org/0000-0001-9757-2432
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
  • Depositors only (login required):

Downloads

Downloads per month over past year