Document Type

Dissertation

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

Thesis Submitted for the degree of Doctor of Philosophy November 2023.

School of Computer Science Technological University Dublin.

Abstract

Human-Robot Interaction (HRI) is an important but challenging field focused on improving the interaction between humans and robots, to make the interaction more intelligent and effective. However, building a natural conversational HRI is an interdisciplinary challenge for scholars, engineers, and designers. Achieving successful conversational interaction with a social robot necessitates not only observing a user’s active participation in the interaction but also being aware of their emotional and attitudinal states as the interaction progresses. On the topic of attitudinal states, one field that has received little attention to date is monitoring the user for possible confusion states. Confusion is a nontrivial mental state related to mental workload that can be seen as having at least two distinct substates, i.e., productive confusion and unproductive confusion. Of these two, unproductive confusion is the most disruptive as individuals in an unproductive state of confusion may lose their engagement and motivation to overcome obstacles, potentially resulting in disengagement from the conversation or even abandoning an interaction altogether. Although there have been some research efforts focused on monitoring and detecting confusion, the domain of such research has to date been limited, primarily centring around evaluating confusion states in online learning tasks.

DOI

https://doi.org/10.21427/0vrj-n729

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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