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Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications

Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications

Tiong Yew Tang, Simon Egerton, János Botzheim
Copyright: © 2019 |Pages: 20
ISBN13: 9781522580607|ISBN10: 1522580603|EISBN13: 9781522580614
DOI: 10.4018/978-1-5225-8060-7.ch049
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MLA

Tang, Tiong Yew, et al. "Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications." Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 1047-1066. https://doi.org/10.4018/978-1-5225-8060-7.ch049

APA

Tang, T. Y., Egerton, S., & Botzheim, J. (2019). Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications. In I. Management Association (Ed.), Rapid Automation: Concepts, Methodologies, Tools, and Applications (pp. 1047-1066). IGI Global. https://doi.org/10.4018/978-1-5225-8060-7.ch049

Chicago

Tang, Tiong Yew, Simon Egerton, and János Botzheim. "Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications." In Rapid Automation: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1047-1066. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8060-7.ch049

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Abstract

In a real-world environment, a social robot is constantly required to make many critical decisions in an ambiguous and demanding (stressful) environment. Hence, a biological stress response system model is a good gauge indicator to judge when the robot should react to such environment and adapt itself towards the environment changes. This work is to implement the Smerek's reflective processing model into human-robot communication application where reflective processing is triggered during such situations where the best action is not known. The authors want to investigate how to address better the human-robot communication problems with the focus on reflective processing model in the perspectives of working memory, Spiking Neural Network (SNN) and stress response system. The authors had applied their proposed Spiking Reflective Processing model for the human-robot communication application in a university population. The initial experimental results showed the positive attitude changes before and after the human-robot interaction experiment.

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