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Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task
(Sage, 2021-02)
Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed ...
Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
(IEEE, 2021-08-08)
Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in ...
Modeling robot trust based on emergent emotion in an interactive task
(IEEE, 2021)
Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing ...
Multimodal reinforcement learning for partner specific adaptation in robot-multi-robot interaction
(IEEE, 2022)
Successful and efficient teamwork requires knowledge of the individual team members' expertise. Such knowledge is typically acquired in social interaction and forms the basis for socially intelligent, partner-Adapted ...
Trustworthiness assessment in multimodal human-robot interaction based on cognitive load
(IEEE, 2022)
In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner ...
Trust in robot–robot scaffolding
(IEEE, 2023-12-01)
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 ...
Interplay between neural computational energy and multimodal processing in robot-robot interaction
(IEEE, 2023)
Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not ...
Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework
(IEEE, 2023)
Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective - that is, forming robot trust in interaction partners. Our previous ...
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