Forming robot trust in heterogeneous agents during a multimodal interactive game

Name: Forming robot trust in heterogeneous agents during a multimodal interactive game
Author: Kırtay, M., Öztop, Erhan, Kuhlen, A. K., Asada, M., Hafner, V. V.

İsim	Forming robot trust in heterogeneous agents during a multimodal interactive game
Yazar	Kırtay, M., Öztop, Erhan, Kuhlen, A. K., Asada, M., Hafner, V. V.
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Heterogeneous interaction, Internal reward, Multimodal integration, Trust
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166541311-4
Kayıt Numarası	8ae2732d-afc7-495d-ad59-3339722d135b
Lokasyon	Computer Science
Tarih	2022
Notlar	Deutsche Forschungsgemeinschaft ; Osaka University
Örnek Metin	This study presents a robot trust model based on cognitive load that uses multimodal cues in a learning setting to assess the trustworthiness of heterogeneous interaction partners. As a test-bed, we designed an interactive task where a small humanoid robot, Nao, is asked to perform a sequential audio-visual pattern recall task while minimizing its cognitive load by receiving help from its interaction partner, either a robot, Pepper, or a human. The partner displayed one of three guiding strategies, reliable, unreliable, or random. The robot is equipped with two cognitive modules: a multimodal auto-associative memory and an internal reward module. The former represents the multimodal cognitive processing of the robot and allows a 'cognitive load' or 'cost' to be assigned to the processing that takes place, while the latter converts the cognitive processing cost to an internal reward signal that drives the cost-based behavior learning. Here, the robot asks for help from its interaction partner when its action leads to a high cognitive load. Then the robot receives an action suggestion from the partner and follows it. After performing interactive experiments with each partner, the robot uses the cognitive load yielded during the interaction to assess the trustworthiness of the partners -i.e., it associates high trustworthiness with low cognitive load. We then give a free choice to the robot to select the trustworthy interaction partner to perform the next task. Our results show that, overall, the robot selects partners with reliable guiding strategies. Moreover, the robot's ability to identify a trustworthy partner was unaffected by whether the partner was a human or a robot.
DOI	10.1109/ICDL53763.2022.9962212

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Forming robot trust in heterogeneous agents during a multimodal interactive game

Yazar Kırtay, M., Öztop, Erhan, Kuhlen, A. K., Asada, M., Hafner, V. V.

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Heterogeneous interaction, Internal reward, Multimodal integration, Trust

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166541311-4

Kayıt Numarası 8ae2732d-afc7-495d-ad59-3339722d135b

Lokasyon Computer Science

Tarih 2022

Notlar Deutsche Forschungsgemeinschaft ; Osaka University

Örnek Metin This study presents a robot trust model based on cognitive load that uses multimodal cues in a learning setting to assess the trustworthiness of heterogeneous interaction partners. As a test-bed, we designed an interactive task where a small humanoid robot, Nao, is asked to perform a sequential audio-visual pattern recall task while minimizing its cognitive load by receiving help from its interaction partner, either a robot, Pepper, or a human. The partner displayed one of three guiding strategies, reliable, unreliable, or random. The robot is equipped with two cognitive modules: a multimodal auto-associative memory and an internal reward module. The former represents the multimodal cognitive processing of the robot and allows a 'cognitive load' or 'cost' to be assigned to the processing that takes place, while the latter converts the cognitive processing cost to an internal reward signal that drives the cost-based behavior learning. Here, the robot asks for help from its interaction partner when its action leads to a high cognitive load. Then the robot receives an action suggestion from the partner and follows it. After performing interactive experiments with each partner, the robot uses the cognitive load yielded during the interaction to assess the trustworthiness of the partners -i.e., it associates high trustworthiness with low cognitive load. We then give a free choice to the robot to select the trustworthy interaction partner to perform the next task. Our results show that, overall, the robot selects partners with reliable guiding strategies. Moreover, the robot's ability to identify a trustworthy partner was unaffected by whether the partner was a human or a robot.

DOI 10.1109/ICDL53763.2022.9962212