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Advances in Intelligence-Empowered Technologies
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Advances in Intelligence-Empowered Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 915

Editors

Dr. Zheng Liu

E-Mail Website
Guest Editor
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China
Interests: multilingual artificial intelligence applications; multimodal learning; preservation of digital culture heritage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu Weng

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Taipa, Macau
Interests: artificial intelligence; multimodal learning; digital humanities
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Liu

E-Mail Website
Guest Editor
Hainan International College, Minzu University of China, Hainan 572423, China
Interests: cloud computing; optimization

Special Issue Information

Dear Colleagues,

Intelligence-empowered technologies are reshaping the global sci-tech ecosystem and social fabric at an unprecedented rate, emerging as the core engine propelling a new wave of productivity revolution and driving the deep integration of artificial intelligence with diverse industrial sectors. As the foundational pillar of the intelligent era, data intelligence tackles the longstanding challenges of data silos and fragmented knowledge association through key technologies such as federated learning and knowledge graphs, thereby forging robust data underpinning the deployment of intelligent systems across healthcare, finance, smart cities, and other vital domains. Computational intelligence, the pivotal driver of efficiency enhancement, transcends the inherent limitations of traditional computing power by leveraging advancements in cloud computing scheduling algorithms and computer vision technologies. It converts raw data value into actionable, complex decision-making capabilities and accelerates the paradigm shift of productivity from experience-driven to algorithm-driven across industrial manufacturing, autonomous transportation, and quality inspection scenarios. As the ultimate pursuit of human–machine symbiosis, cultural intelligence enables in-depth synergy between technological innovation and civilizational development via multilingual artificial intelligence and image–text-editing technologies. It bridges cross-lingual and cross-cultural digital divides and marks the evolution of artificial intelligence from a purely instrumental rationality to a more holistic value rationality, embedding humanistic care and ethical considerations into technological advancement.

This Special Issue is dedicated to compiling cutting-edge research findings, innovative methodological explorations, and real-world applications of intelligence-empowered technologies. We seek to further advance the theoretical innovation and practical application of intelligence-empowered technologies and explore new paradigms for their sustainable development in the digital age.

Dr. Zheng Liu
Prof. Dr. Yu Weng
Dr. Xuan Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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

  • intelligence-empowered technologies
  • data intelligence
  • computational intelligence
  • cultural intelligence
  • multilingual artificial intelligence

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Published Papers (1 paper)

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Research

47 pages, 2483 KB  
Article
Geometry-Aware Conformal Temperature Calibration for Entropic Action Selection in Reinforcement Learning
by J. Ernesto Solanes and Aitana Francés-Falip
Electronics 2026, 15(14), 3192; https://doi.org/10.3390/electronics15143192 - 20 Jul 2026
Viewed by 370
Abstract
Entropy-based soft-min operators and Gibbs policies are widely used in reinforcement learning to smooth greedy decisions and regulate stochastic action selection. Their behavior depends critically on an inverse temperature parameter, which is often chosen through fixed values, annealing schedules, entropy targets, or worst-case [...] Read more.
Entropy-based soft-min operators and Gibbs policies are widely used in reinforcement learning to smooth greedy decisions and regulate stochastic action selection. Their behavior depends critically on an inverse temperature parameter, which is often chosen through fixed values, annealing schedules, entropy targets, or worst-case bounds. These rules do not directly control the local error induced by soft action selection and do not account for the geometry of the action-value vector. This paper develops a geometry-aware conformal calibration framework for selecting inverse temperatures in discounted finite-action reinforcement learning. The analysis distinguishes the operator-level soft-min approximation error from decision-level Gibbs excess and shows how both quantities depend on local value gaps and a near-optimal action structure. A conformal order statistic rule is then used to obtain finite-sample marginal control of the selected local score under exchangeability. A geometry-conditional extension assigns different temperatures to different action-value geometries. The Bellman analysis clarifies that fixed temperature maps preserve contraction, while data-dependent conformal selectors are best interpreted as post-training action selection rules. A finite-MDP experiment empirically confirms the conformal coverage behavior and shows that calibration must be applied to a decision-relevant cost representation when value estimates contain optimistic value estimation traps. An Atari SpaceInvaders experiment with a frozen deep Q-network shows that the learned action-value geometry is heterogeneous, that decision-level calibration is less conservative than operator-level calibration, and that gated geometry-conditional selection preserves most of the greedy return while providing explicit marginal score control of randomized decision excess with low online overhead. Full article
(This article belongs to the Special Issue Advances in Intelligence-Empowered Technologies)
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