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The Essence of Ethical Reasoning in Robot-Emotion Processing

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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.

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Notes

  1. Our focus in the remaining of the paper will be more inclined towards autonomous robots implementing emotion models.

  2. While the definition of what is socially acceptable might vary between cultures, our definition of socially acceptable emotions focuses on the context of interaction between human and a robot as presented in earlier examples.

  3. By saying ethical standards, we mean what a person believes as right or wrong from the ethical standpoint.

  4. According to Appraisal theory, emotions result from the evaluation of the given situation which needs deliberate thinking from the individual [26, 32].

  5. According to Appraisal theory, an event results in triggering of more than one emotions at the same time [26].

  6. While another form of ethical theory called virtue ethics exists [14], it is mostly descriptive in nature and not feasible to be realised in artificial agents like social robots. Therefore, we shall not indulge into the discussion of virtue ethics in this paper.

  7. Although literature suggests that mood and personality play a dynamic role in the process of emotion generation, in this paper, we shall not discuss the relationship of mood and personality with emotion. We have integrated the notion of mood and personality in EEGS and currently investigating the relationship of those factors in the process of emotion generation.

  8. Detailed discussion about how emotions are differentiated with varying values for the degree of their positivity and negativity is out of the scope of this paper. For further discussion on the degrees of valence of different emotions, please refer to [28] and related literature.

  9. While the signed value of Degree was sufficient to specify the Valence as POSITIVE or NEGATIVE, we chose to consider “Valence” as an explicit parameter for the ease of computational mechanism.

  10. The range of \([-\,1, +\,1]\) is a subjective choice. It is completely feasible to select other ranges like \([-\,10, +\,10]\) or \([-\,100, +\,100]\).

  11. We could not find strong evidence on how long the decay time should be considered for an emotion. However, most existing emotion models were found to use the decay time of less then 10 s.

  12. In the examples of previous paragraph, the Source was the robot itself. We have used the notion of Source to allow EEGS to be able to store also the standards about what it believes one person should behave with another person. This kind of design helps EEGS to perform ethical reasoning when two other persons recognised by it interact with each other. This property can be extremely useful in situations of multi-agent interaction.

  13. While the scenarios were designed by the subjects, the emotion generation mechanism was dynamic and determined by the emotion system itself during the interaction.

  14. Dementia is a mental condition in which a person experiences a gradual decrease in the ability to think and remember even the things of normal daily life.

  15. See Table 2 for examples of actions from Rose to Lily.

  16. As mentioned in Sect. 6.1, emotion intensities in EEGS lie in the range [0, 1], where 0 signifies very low intensity and 1 signifies very high intensity.

  17. While we have used the Quantified Emotion as a measure of emotion dynamics in this paper, using only the emotion intensity considering the sign for positive or negative emotions also provided similar results.

  18. It is reasonable to argue that it is not always ethical to have lowered negativity in emotional responses which can occur due to bias of an individual in favour of his/her loved ones. However, in situation of social interaction as in the case of Rose and Lily, it is desirable to have lowered negativity in emotional responses.

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Acknowledgements

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

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Authors and Affiliations

  1. Centre for Artificial Intelligence (CAI), University of Technology Sydney, Sydney, Australia

    Suman Ojha, Mary-Anne Williams & Benjamin Johnston

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  1. Suman Ojha
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  2. Mary-Anne Williams
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Correspondence to Suman Ojha.

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

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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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Ojha, S., Williams, MA. & Johnston, B. The Essence of Ethical Reasoning in Robot-Emotion Processing. Int J of Soc Robotics 10, 211–223 (2018). https://doi.org/10.1007/s12369-017-0459-y

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