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

Caroline Rizzi; Colin G. Johnson; Patricia A Vargas

doi:10.1109/ROMAN.2016.7745201

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

Caroline Rizzi, Colin G. Johnson, Patricia A Vargas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

22 Downloads (Pure)

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.

Original language	English
Title of host publication	25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Publisher	IEEE
Pages	736-742
Number of pages	7
ISBN (Electronic)	9781509039296
DOIs	https://doi.org/10.1109/ROMAN.2016.7745201
Publication status	Published - 17 Nov 2016
Event	25th IEEE International Symposium on Robot and Human Interactive Communication 2016 - New York, United States Duration: 26 Aug 2016 → 31 Aug 2016

Publication series

Name	IEEE RO-MAN
Publisher	IEEE
ISSN (Print)	1944-9437

Conference

Conference	25th IEEE International Symposium on Robot and Human Interactive Communication 2016
Abbreviated title	RO-MAN 2016
Country/Territory	United States
City	New York
Period	26/08/16 → 31/08/16

ASJC Scopus subject areas

Artificial Intelligence
Social Psychology
Human-Computer Interaction

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10.1109/ROMAN.2016.7745201

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Cite this

@inproceedings{c17797700d014a1eb626cd2313c53138,

title = "Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model",

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.",

author = "Caroline Rizzi and Johnson, {Colin G.} and Vargas, {Patricia A}",

year = "2016",

month = nov,

day = "17",

doi = "10.1109/ROMAN.2016.7745201",

language = "English",

series = "IEEE RO-MAN",

publisher = "IEEE",

pages = "736--742",

booktitle = "25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)",

address = "United States",

note = "25th IEEE International Symposium on Robot and Human Interactive Communication 2016, RO-MAN 2016 ; Conference date: 26-08-2016 Through 31-08-2016",

}

Rizzi, C, Johnson, CG & Vargas, PA 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). IEEE RO-MAN, IEEE, pp. 736-742, 25th IEEE International Symposium on Robot and Human Interactive Communication 2016, New York, United States, 26/08/16. https://doi.org/10.1109/ROMAN.2016.7745201

TY - GEN

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AU - Johnson, Colin G.

AU - Vargas, Patricia A

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

PB - IEEE

Y2 - 26 August 2016 through 31 August 2016

ER -

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

Abstract

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Conference

ASJC Scopus subject areas

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