An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

Charlotte Commu, Mathilde Theelen, J. Treur

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

Abstract

In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.
LanguageEnglish
Title of host publicationHighlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2
PublisherSpringer Verlag
Pages257-270
Number of pages14
StatePublished - 21 Jun 2017

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume722

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Commu, C., Theelen, M., & Treur, J. (2017). An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. In Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2 (pp. 257-270). (Communications in Computer and Information Science; Vol. 722). Springer Verlag.
Commu, Charlotte ; Theelen, Mathilde ; Treur, J./ An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2. Springer Verlag, 2017. pp. 257-270 (Communications in Computer and Information Science).
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abstract = "In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.",
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Commu, C, Theelen, M & Treur, J 2017, An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. in Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2. Communications in Computer and Information Science, vol. 722, Springer Verlag, pp. 257-270.

An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. / Commu, Charlotte; Theelen, Mathilde; Treur, J.

Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2. Springer Verlag, 2017. p. 257-270 (Communications in Computer and Information Science; Vol. 722).

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

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AB - In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

M3 - Conference contribution

T3 - Communications in Computer and Information Science

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BT - Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2

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Commu C, Theelen M, Treur J. An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. In Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. Proceedings of the International Workshop on Multi-Agent Systems for Complex Networks and Social Computation, CNSC'17, Proceedings of PAAMS, vol. 2. Springer Verlag. 2017. p. 257-270. (Communications in Computer and Information Science).