Author
Kırtay, M., Hafner, V. V. V., Asada, Minoru, Öztop, Erhan
Publication Date
2023-12-01
Publication Place
-
IEEE
Subject
Cognitive load, Decision making, Robot trust, Scaffolding, Visual recalling
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2379-8920
Record ID
0afbb7d8-f92b-4e0e-ac09-a9e47996c455
Library Location
Computer Science
Date
2023-12-01
Notes
Deutsche Forschungsgemeinschaft ; Japan Society for the Promotion of Science ; Osaka University
Sample Text
The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce their computational load, which is analogous to human cognitive load. We test this idea by adopting an interactive visual recalling task. In the first set of experiments, the robot can get help from online instructors with different guiding strategies to decide which one it should trust based on the computational load it experiences during the experiments. The second set of experiments involves robot-robot interactions. Akin to the robot-online instructor case, the Pepper robot is asked to scaffold the learning of a less capable 'infant' robot (Nao) with or without being equipped with the cognitive abilities of theory of mind and task experience memory to assess the contribution of these cognitive abilities to scaffolding performance. Overall, the results show that robot trust based on computational/cognitive load within a sequential decision-making framework leads to effective partner selection and robot-robot scaffolding. Thus, using the computational load incurred by the cognitive processing of a robot may serve as an internal signal for assessing the trustworthiness of interaction partners.
DOI
10.1109/TCDS.2023.3235974
Cilt
15
