Improving Interface Design in Interactive Task Learning for Hierarchical Tasks based on a Qualitative Study
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct 2024)
2024
Abstract
Interactive Task Learning (ITL) systems acquire task knowledge from human instructions in natural language interaction. The interaction design of ITL agents for hierarchical tasks stays uncharted. This paper studied Verbal Apprentice Learner(VAL) for gaming, as an ITL example, and qualitatively analyzed the user study data to provide design insights on dialogue language types, task instruction strategies, and error handling. We then proposed an interface design: Editable Hierarchy Knowledge (EHK), as a generic probe for ITL systems for hierarchical tasks.
Topics:Interactive Task LearningHuman-AI Interaction
Projects:Verbal Apprentice Learner (VAL)
BibTeX
@inproceedings{zhou-uist-2024,
title = {Improving Interface Design in Interactive Task Learning for Hierarchical Tasks based on a Qualitative Study},
author = {Zhou, Jieyu and MacLellan, Christopher J.},
booktitle = {Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct 2024)},
year = {2024},
doi = {10.1145/3672539.3686326},
}