Human–Artificial Intelligence (AI) Interaction: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 895

Special Issue Editors


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Guest Editor
Faculty of Information Technology, University of Jyväskylä, FI-40014 Jyväskylä, Finland
Interests: artificial intelligence; complex systems; computer supported cooperative work; human-AI interaction; hybrid intelligent systems; scientometrics; social computing; science and technology studies

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Guest Editor
Postgraduate Program in Informatics (PPGI), Federal University of Rio de Janeiro, Rio de Janeiro 21941-916, Brazil
Interests: computer supported cooperative work; crowdsourcing; digital nomadism; human-computer interaction; social computing; social media

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Guest Editor
INESC TEC, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: collaborative learning; computational thinking; computer supported cooperative work; human-computer interaction; optimization; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Information Technology, University of Jyväskylä, FI-40014 Jyväskylä, Finland
Interests: artificial intelligence; data mining; deep learning; educational technology; learning analytics; machine learning; neural networks

Special Issue Information

Dear Colleagues,

Human-Artificial Intelligence (AI) interaction is on the brink of revolutionizing the world in the coming decades, transforming everything from business operations to household applications. AI empowers systems with the ability to learn, adapt, and make decisions, bringing significant benefits to fields such as medicine, architecture, education, agriculture, and forensics. This transformative technology has redefined the way we interact with the world around us, ushering in a new era of human-AI partnerships where humans use AI-infused systems both implicitly and explicitly to augment their experiences and achieve greater outcomes based on their generative capacity and contextualized meanings in practical uses.

This special issue aims to present the latest advances and perspectives in the area of human-AI interaction. Articles accepted for publication must address topics related to the design, development and evaluation of human-AI interactive systems. We invite both researchers and practitioners to contribute their high-quality original research, reviews, insights, and perspectives on these topics to this special issue.

Topics of interest include but are not limited to:

  • AI models: AI models used for human-AI interaction, such as conversational agents, recommendation systems, and assisted learning systems.
  • User interfaces: user interfaces for human-AI interaction systems, such as natural interfaces, graphical interfaces, and virtual reality-based interfaces.
  • Evaluation of human-AI interactive systems: fieldwork studies (e.g., ethnographically-informed approaches to AI system design) and methods for evaluating human-AI interactive systems such as usability assessment scales, accessibility compliance instruments, and impact assessment methodologies.
  • Challenges and opportunities of human-AI interaction in real-world settings: potential obstacles and possibilities to implementing human-AI systems in specific application domains, such as collaborative clinical work, digital well-being, misinformation, creativity work, and entertainment.

Dr. António Correia
Dr. Daniel Schneider
Prof.Dr. Benjamim Fonseca
Prof.Dr. Tommi Kärkkäinen
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • foundation models
  • human-AI interaction
  • human-centered generative AI
  • hybrid intelligent systems
  • large language models
  • machine learning
  • mixed-initiative systems
  • user experience

Published Papers (1 paper)

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Research

19 pages, 11964 KiB  
Article
Translating Words to Worlds: Zero-Shot Synthesis of 3D Terrain from Textual Descriptions Using Large Language Models
by Guangzi Zhang, Lizhe Chen, Yu Zhang, Yan Liu, Yuyao Ge and Xingquan Cai
Appl. Sci. 2024, 14(8), 3257; https://doi.org/10.3390/app14083257 - 12 Apr 2024
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Abstract
The current research on text-guided 3D synthesis predominantly utilizes complex diffusion models, posing significant challenges in tasks like terrain generation. This study ventures into the direct synthesis of text-to-3D terrain in a zero-shot fashion, circumventing the need for diffusion models. By exploiting the [...] Read more.
The current research on text-guided 3D synthesis predominantly utilizes complex diffusion models, posing significant challenges in tasks like terrain generation. This study ventures into the direct synthesis of text-to-3D terrain in a zero-shot fashion, circumventing the need for diffusion models. By exploiting the large language model’s inherent spatial awareness, we innovatively formulate a method to update existing 3D models through text, thereby enhancing their accuracy. Specifically, we introduce a Gaussian–Voronoi map data structure that converts simplistic map summaries into detailed terrain heightmaps. Employing a chain-of-thought behavior tree approach, which combines action chains and thought trees, the model is guided to analyze a variety of textual inputs and extract relevant terrain data, effectively bridging the gap between textual descriptions and 3D models. Furthermore, we develop a text–terrain re-editing technique utilizing multiagent reasoning, allowing for the dynamic update of the terrain’s representational structure. Our experimental results indicate that this method proficiently interprets the spatial information embedded in the text and generates controllable 3D terrains with superior visual quality. Full article
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