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International Journal of Social Robotics

, Volume 10, Issue 2, pp 211–223 | Cite as

The Essence of Ethical Reasoning in Robot-Emotion Processing

  • Suman Ojha
  • Mary-Anne Williams
  • Benjamin Johnston
Article

Abstract

As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions.

Keywords

Social robots Computational emotion model Believability Ethical reasoning Socially acceptable emotions EEGS 

Notes

Acknowledgements

This research is supported by an Australian Government Research Training Program Scholarship.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest with any organisation in relation to this research.

Funding

This research was funded by the Research Scholarship provided by the University of Technology Sydney. There is no external funding associated with this research.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Centre for Artificial Intelligence (CAI)University of Technology SydneySydneyAustralia

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