A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response

Linh Tran, J. Treur, Denice J. Tuinhof

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inspired by the work of Elaine Aron, in this paper a human-like adaptive computational agent model of the internal processes of a highly sensitive person (HSP) is presented. This agent model was used to get a better understanding of what goes wrong in these internal processes once this person gets upset. A scenario is addressed where a highly sensitive person will get upset by an external stimulus and will not be able to calm down by herself. Yet in a social context the interaction with a second person (without high sensitivity) will calm the HSP down, thus contributing to regulation. To obtain an adaptive model a Hebbian learning connection was integrated. During interaction with a second person this Hebbian learning link will become stronger, which makes it possible for a HSP to become independent after some time and be able to regulate upsetting external stimuli all by herself.
LanguageEnglish
Title of host publicationProceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18
PublisherSpringer Verlag
StatePublished - 20 Jun 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Cite this

Tran, L., Treur, J., & Tuinhof , D. J. (2018). A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. In Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18 (Lecture Notes in Computer Science). Springer Verlag.
Tran, Linh ; Treur, J. ; Tuinhof , Denice J. . / A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18. Springer Verlag, 2018. (Lecture Notes in Computer Science).
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Tran, L, Treur, J & Tuinhof , DJ 2018, A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. in Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18. Lecture Notes in Computer Science, Springer Verlag.

A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. / Tran, Linh; Treur, J.; Tuinhof , Denice J. .

Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18. Springer Verlag, 2018. (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Tuinhof ,Denice J.

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N2 - Inspired by the work of Elaine Aron, in this paper a human-like adaptive computational agent model of the internal processes of a highly sensitive person (HSP) is presented. This agent model was used to get a better understanding of what goes wrong in these internal processes once this person gets upset. A scenario is addressed where a highly sensitive person will get upset by an external stimulus and will not be able to calm down by herself. Yet in a social context the interaction with a second person (without high sensitivity) will calm the HSP down, thus contributing to regulation. To obtain an adaptive model a Hebbian learning connection was integrated. During interaction with a second person this Hebbian learning link will become stronger, which makes it possible for a HSP to become independent after some time and be able to regulate upsetting external stimuli all by herself.

AB - Inspired by the work of Elaine Aron, in this paper a human-like adaptive computational agent model of the internal processes of a highly sensitive person (HSP) is presented. This agent model was used to get a better understanding of what goes wrong in these internal processes once this person gets upset. A scenario is addressed where a highly sensitive person will get upset by an external stimulus and will not be able to calm down by herself. Yet in a social context the interaction with a second person (without high sensitivity) will calm the HSP down, thus contributing to regulation. To obtain an adaptive model a Hebbian learning connection was integrated. During interaction with a second person this Hebbian learning link will become stronger, which makes it possible for a HSP to become independent after some time and be able to regulate upsetting external stimuli all by herself.

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Tran L, Treur J, Tuinhof DJ. A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. In Proceedings of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS'18. Springer Verlag. 2018. (Lecture Notes in Computer Science).