Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications
| Autor: | Janos Botzheim, Tiong Yew Tang, Simon Egerton |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Stress (mechanics)
Computer science business.industry 05 social sciences 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences 02 engineering and technology Artificial intelligence business 050105 experimental psychology |
| Zdroj: | International Journal of Artificial Life Research. 7:67-84 |
| ISSN: | 1947-3079 1947-3087 |
| DOI: | 10.4018/ijalr.2017010105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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