A Situation-Aware Fear Learning (SAFEL) Model for Robots
- Submitting institution
-
Heriot-Watt University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15394858
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2016.09.035
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 32
- Volume
- 221
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper deals with a long-standing problem in autonomous interactive systems (not only robots) of adapting action selection to the current context; a live issue for instance in successfully fielding autonomous vehicles. It is part of a flourishing field in which models of affect/emotion are applied to this problem and has produced a novel architecture which has already been well received. Some of its ideas are bring taken up by researchers in autonomous vehicles, multiple unmanned vehicles and aircraft, Robocupo and the control of microgrids.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
