Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.3
Journals
Mathematics
Special Issues
Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control
share announcement

Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 1907

Editors

Dr. Changdong Yu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Dalian Maritime University, Dalian 116024, China
Interests: deep learning; reinforcement learning; computer vision; unmanned system decision-making
Prof. Dr. Xiao Liang

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, China
Interests: ship hydrodynamics; unmanned system technology; decision-making
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojie Sun

E-Mail Website
Guest Editor
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
Interests: decision-making; autonomous control systems; multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue, titled “Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control,” aims to explore the intersection and cutting-edge advances of mathematical methods and machine learning techniques in intelligent perception, decision-making, and control systems. The focus lies in how mathematical modeling, intelligent algorithms, and data-driven approaches can enhance the perception accuracy, decision-making efficiency, and control robustness of intelligent systems.

In the context of rapid advancements in intelligent systems, perception, decision-making, and control constitute the core link from information acquisition to task execution. This issue emphasizes how advanced mathematical theories—such as optimization, nonlinear systems analysis, game theory, and probabilistic reasoning—can be integrated with machine learning models, including deep learning, reinforcement learning, and generative models, to promote adaptive and autonomous evolution in intelligent systems.

Intelligent perception provides the foundation for environmental understanding and information fusion. By introducing multimodal data processing, pattern recognition, and visual computing techniques, intelligent systems can achieve high-precision situational awareness in complex and uncertain environments. In the decision-making and control phases, frameworks that combine machine learning with optimal control enable closed-loop optimization from data to action, ensuring stability and efficiency under dynamic missions and disturbance conditions.

Moreover, this issue also focuses on integrated research based on mathematical optimization, game-theoretic modeling, and learning-driven control, highlighting collaborative decision-making in multi-agent systems, strategic interactions in complex environments, and the safety and resilience control of intelligent systems. Through the deep integration of mathematical theory and artificial intelligence algorithms, this special issue seeks to advance the dual breakthroughs of performance enhancement and intelligence evolution in next-generation intelligent systems—covering unmanned surface vessels, underwater vehicles, unmanned aerial vehicles, as well as robotics, autonomous transportation, and industrial intelligence.

Overall, this special issue aims to showcase innovative achievements in mathematics and machine learning for intelligent perception, decision-making, and control, fostering interdisciplinary integration and theoretical innovation. We sincerely invite researchers from mathematics, artificial intelligence, control, and systems science to join us in exploring frontier challenges and laying the theoretical and methodological foundation for building smarter, safer, and more efficient autonomous systems of the future.

Dr. Changdong Yu
Prof. Dr. Xiao Liang
Dr. Xiaojie Sun
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. Mathematics 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 2600 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

  • mathematical modeling
  • deep learning
  • reinforcement learning
  • intelligent perception
  • computer vision
  • decision-making
  • control systems
  • optimization
  • multi-agent systems
  • autonomous systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 1776 KB  
Article
AGTA: Topology-Aware Sequential Decision-Making in Multi-Agent Reinforcement Learning
by Kun Hu, Shanghua Wen, Wendi Wu, Xiang Zhang and Minglong Li
Mathematics 2026, 14(16), 2907; https://doi.org/10.3390/math14162907 - 12 Aug 2026
Viewed by 257
Abstract
Multi-agent reinforcement learning (MARL) has long grappled with fundamental coordination challenges due to the existence of complex inter-agent correlations that are inherent in multi-agent systems. While the recent advancement of the sequential decision-making paradigm provides fine-grained supervision for the multi-agent decision-making process, the [...] Read more.
Multi-agent reinforcement learning (MARL) has long grappled with fundamental coordination challenges due to the existence of complex inter-agent correlations that are inherent in multi-agent systems. While the recent advancement of the sequential decision-making paradigm provides fine-grained supervision for the multi-agent decision-making process, the absence of computationally tractable solutions for inter-agent correlation management remains a critical challenge and substantially constrains the impact of this paradigm. To tackle this challenge, in this paper, we introduce Action Generation with Topology Awareness (AGTA), a topology-aware sequential decision-making framework in MARL that integrates inter-agent correlation modeling with topology-guided decision-order optimization. AGTA extracts inter-agent mutual attention via multi-agent transformer during the learning dynamics. Subsequently, it captures directed acyclic graphs(DAGs) directly from the extracted attention matrices to model inter-agent correlations. Finally, it refines the action generation order by analyzing and solving topological constraints, thus realizing inter-agent correlation management. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art counterparts. Full article
(This article belongs to the Special Issue Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control)
►▼ Show Figures

Figure 1

26 pages, 2652 KB  
Article
Data-Aware Locality-Sensitive Hashing with Theoretical Guarantees for Approximate Nearest Neighbor Search
by Zongyuan Tan, Jihuan Wang, Hongya Wang, Zhaoxing Xu and Ning Cai
Mathematics 2026, 14(12), 2046; https://doi.org/10.3390/math14122046 - 8 Jun 2026
Viewed by 390
Abstract
Locality-sensitive hashing (LSH) has been widely used for c-approximate nearest neighbor search (c-ANNS) in high-dimensional spaces. However, its retrieval performance degrades as the dataset size grows. To address this limitation, we propose a data-dependent hashing framework called data-aware locality-sensitive hashing [...] Read more.
Locality-sensitive hashing (LSH) has been widely used for c-approximate nearest neighbor search (c-ANNS) in high-dimensional spaces. However, its retrieval performance degrades as the dataset size grows. To address this limitation, we propose a data-dependent hashing framework called data-aware locality-sensitive hashing (DASH). DASH exploits the quantization properties of product quantization (PQ) to learn a data-aware residual prior, enabling adaptive, data-sensitive LSH. By converting exact Euclidean distance computations into efficient table lookup operations, DASH reduces computational costs and enhances retrieval efficiency. Based on the residual prior, DASH provides a theoretical performance guarantee comparable to that of standard LSH. Extensive experiments on multiple benchmark datasets demonstrate that DASH consistently achieves superior search accuracy and efficiency, yielding up to 40× speedups over various state-of-the-art baselines. Full article
(This article belongs to the Special Issue Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control)
►▼ Show Figures

Figure 1

24 pages, 12396 KB  
Article
YOLO-CAB: An Efficient Deep Learning-Based Underwater Object Detection Method for Autonomous Underwater Vehicles
by Runze Li, Changdong Yu, Shuaiyu Bao, Zijian Li and Jinyi Yao
Mathematics 2026, 14(11), 1927; https://doi.org/10.3390/math14111927 - 2 Jun 2026
Cited by 1 | Viewed by 601
Abstract
High-precision environmental perception is essential for deep-sea exploration and autonomous underwater vehicle operations. However, physical factors such as light scattering and selective absorption, along with high target–background similarity, cause existing detection methods to suffer from low recall and inaccurate localization on targets with [...] Read more.
High-precision environmental perception is essential for deep-sea exploration and autonomous underwater vehicle operations. However, physical factors such as light scattering and selective absorption, along with high target–background similarity, cause existing detection methods to suffer from low recall and inaccurate localization on targets with blurred edges, low contrast, or category ambiguity. To address these challenges, we propose YOLO-CAB, a YOLOv13-based underwater object detector designed to handle these complex scenes by optimizing feature extraction, boundary perception, and the loss function. First, we introduce the Context-Aware Large Selective Kernel (CALSK) module into the shallow backbone layers to expand the receptive field and adaptively enhance spatial features via multi-scale depthwise convolutions. Furthermore, the Spatial Boundary Attention Module (SBAM) is applied before the feature pyramid enters the detection head to refine multi-scale features and enhance sensitivity to target boundaries. To address the variance in detection difficulty across categories, we also develop the Momentum-based Category-Aware Weighted Intersection over Union (MCAWIoU) loss. Consequently, the proposed weighting mechanism improves localization accuracy and confidence distribution for challenging samples. Evaluated on the RUOD benchmark dataset, YOLO-CAB improves mean average precision (mAP50) by 4.67%, the stricter localization metric (mAP50-95) by 3.0%, and F1 score by 3.7% over the vanilla YOLOv13n baseline. Ablation studies confirm the individual and synergistic contributions of these components. With 8.85 GFLOPs and an inference time of 5.21 ms under the tested hardware setting, YOLO-CAB improves detection accuracy while maintaining real-time inference. Full article
(This article belongs to the Special Issue Mathematics and Machine Learning for Intelligent Perception, Decision-Making, and Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop