AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

How to

Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.

Show all

2024

He, Z.; Li, S.; Song, Y.; Cai, Z.

Towards Building Condition-Based Cross-Modality Intention-Aware Human-AI Cooperation under VR Environment Proceedings Article

In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2024, ISBN: 979-840070330-0 (ISBN).

Abstract | Links | BibTeX | Tags: Action Generation, Building conditions, Condition, Critical challenges, Cross modality, Human-AI Cooperation, Information presentation, Intention Detection, Language Model, Multi-modal, Purchasing, User interfaces, Virtual Reality

@inproceedings{he_towards_2024,

title = {Towards Building Condition-Based Cross-Modality Intention-Aware Human-AI Cooperation under VR Environment},

author = {Z. He and S. Li and Y. Song and Z. Cai},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194829231&doi=10.1145%2f3613904.3642360&partnerID=40&md5=44d237a6e2a686af74ffb684ef887ab6},

doi = {10.1145/3613904.3642360},

isbn = {979-840070330-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {To address critical challenges in effectively identifying user intent and forming relevant information presentations and recommendations in VR environments, we propose an innovative condition-based multi-modal human-AI cooperation framework. It highlights the intent tuples (intent, condition, intent prompt, action prompt) and 2-Large-Language-Models (2-LLMs) architecture. This design, utilizes “condition” as the core to describe tasks, dynamically match user interactions with intentions, and empower generations of various tailored multi-modal AI responses. The architecture of 2-LLMs separates the roles of intent detection and action generation, decreasing the prompt length and helping with generating appropriate responses. We implemented a VR-based intelligent furniture purchasing system based on the proposed framework and conducted a three-phase comparative user study. The results conclusively demonstrate the system's superiority in time efficiency and accuracy, intention conveyance improvements, effective product acquisitions, and user satisfaction and cooperation preference. Our framework provides a promising approach towards personalized and efficient user experiences in VR. © 2024 Copyright held by the owner/author(s)},

keywords = {Action Generation, Building conditions, Condition, Critical challenges, Cross modality, Human-AI Cooperation, Information presentation, Intention Detection, Language Model, Multi-modal, Purchasing, User interfaces, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}