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Electronics
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Data-Driven Intelligence in Autonomous Systems
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Data-Driven Intelligence in Autonomous Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 7714

Special Issue Editors

Dr. Yingke Chen

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE7 7YT, UK
Interests: artificial intelligence; multiagent systems; machine learning and formal verification
Dr. Xu Wang

E-Mail Website
Guest Editor
College of Computer Science, Sichuan University, Chengdu 610065, China
Interests: machine learning; multi-modal learning; cross-domain learning
Prof. Dr. Yifeng Zeng

E-Mail Website
Guest Editor
School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle NE7 7YT, UK
Interests: intelligent agents; probabilistic graphical models; computational intelligence; digital education and social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous systems (ASs) have been established as key technologies in developing intelligent systems. In an autonomous system, components (also known as agents) interact with each other to achieve common or individual goals. It is essential for ASs to be flexible and adaptive to deal with complex, dynamic, and changing environments, and to be capable of improving their performance through learning and interaction with its counterparts. Autonomous systems can be represented as hardware and software systems, such as robots, vehicles, drones, social networks, smart grids, manufacturing processes, and virtual assistants. Due to such diverse applications of autonomous systems, data-driven solutions form a promising pathway to support their intelligent capabilities, on top of traditional reasoning and planning techniques.

In this Special Issue, ‘Data-Driven Intelligence in Autonomous Systems’, we welcome the latest results of data-driven computational solutions which are applicable to autonomous systems. While the list is not exhaustive, some suggested themes for submissions include the following:

  • Multiagent systems, including decision making and mechanism design.
  • Large foundation models in autonomous systems, particularly with reasoning capabilities.
  • Machine learning, including deep learning and reinforcement learning.
  • Multimodal analysis.
  • Neural architecture search.
  • Natural language processing.
  • Computer vision.
  • System identification, including anomaly detection.
  • System optimisation, including both parameter and structural optimisation.
  • Partial observability in autonomous systems, including latent and blind signal separation.

We also welcome successful applications formulated as autonomous systems such as:

  • Networked systems such as social networks, smart grids, connected autonomous vehicles, and drones;
  • Computer games including serious games and digital twins;
  • Digital manufacturing;
  • Intelligent tutor systems;
  • Granular computing.

Dr. Yingke Chen
Dr. Xu Wang
Prof. Dr. Yifeng Zeng
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. 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

  • artificial intelligence
  • autonomous systems
  • machine learning
  • optimisation
  • signal processing
  • data fusion
  • decision making
  • uncertainty reasoning

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Published Papers (6 papers)

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Research

17 pages, 709 KiB  
Article
Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience Model
by Huifan Gao and Biyang Ma
Electronics 2024, 13(19), 3858; https://doi.org/10.3390/electronics13193858 - 29 Sep 2024
Viewed by 431
Abstract
Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can also be used for real-time updates of learning strategies, their update [...] Read more.
Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can also be used for real-time updates of learning strategies, their update efficiency is not high, making it difficult to capture the characteristics exhibited by learners during the learning process in a timely manner. With the superior performance of the Partially Observable Markov Decision Process (POMDP), this paper proposes a POMDP-based cognitive experience model, which can be quickly updated during interactions and enables the real-time induction of learning strategies by weighting the learning experiences of different learners. Experimental results demonstrate that the learning strategies induced by PCEM are more personalized and exhibit superior performance. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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15 pages, 817 KiB  
Article
Exploration of Deep-Learning-Based Approaches for False Fact Identification in Social Judicial Systems
by Yuzhuo Zou, Jiepin Chen, Jiebin Cai, Mengen Zhou and Yinghui Pan
Electronics 2024, 13(19), 3831; https://doi.org/10.3390/electronics13193831 - 27 Sep 2024
Viewed by 623
Abstract
With the many applications of artificial intelligence (AI) in social judicial systems, false fact identification becomes a challenging issue when the system is expected to be more autonomous and intelligent in assisting a judicial review. In particular, private lending disputes often involve false [...] Read more.
With the many applications of artificial intelligence (AI) in social judicial systems, false fact identification becomes a challenging issue when the system is expected to be more autonomous and intelligent in assisting a judicial review. In particular, private lending disputes often involve false facts that are intentionally concealed and manipulated due to unique and dynamic relationships and their nonconfrontational nature in the judicial system. In this article, we investigate deep learning techniques to identify false facts in loan cases for the purpose of reducing the judicial workload. Specifically, we adapt deep-learning-based natural language processing techniques to a dataset over 100 real-world judicial rules spanning four courts of different levels in China. The BERT (bidirectional encoder representations from transformers)-based classifier and T5 text generation models were trained to classify false litigation claims semantically. The experimental results demonstrate that T5 has a robust learning capability with a small number of legal text samples, outperforms BERT in identifying falsified facts, and provides explainable decisions to judges. This research shows that deep-learning-based false fact identification approaches provide promising solutions for addressing concealed information and manipulation in private lending lawsuits. This highlights the feasibility of deep learning to strengthen fact-finding and reduce labor costs in the judicial field. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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18 pages, 2560 KiB  
Article
An Improved Co-Resident Attack Defense Strategy Based on Multi-Level Tenant Classification in Public Cloud Platforms
by Yuxi Peng, Xinchen Jiang, Shaoming Wang, Yanping Xiang and Liudong Xing
Electronics 2024, 13(16), 3273; https://doi.org/10.3390/electronics13163273 - 18 Aug 2024
Viewed by 816
Abstract
Co-resident attacks are serious security threats in multi-tenant public cloud platforms. They are often implemented by building side channels between virtual machines (VMs) hosted on the same cloud server. Traditional defense methods are troubled by the deployment cost. The existing tenant classification methods [...] Read more.
Co-resident attacks are serious security threats in multi-tenant public cloud platforms. They are often implemented by building side channels between virtual machines (VMs) hosted on the same cloud server. Traditional defense methods are troubled by the deployment cost. The existing tenant classification methods can hardly cope with the real dataset that is quite large and extremely unevenly distributed, and may have problems in the processing speed considering the computation complexity of the DBSCAN algorithm. In this paper, we propose a novel co-resident attack defense strategy which solve these problems through an improved and efficient multi-level clustering algorithm and semi-supervised classification method. We propose a novel multi-level clustering algorithm which can efficiently reduce the complexity, since only a few parameter adjustments are required. Built on the proposed clustering algorithm, a semi-supervised classification model is designed. The experimental results of the classification effect and training speed show that our model achieves F-scores of over 85% and is significantly faster than traditional SVM classification methods. Based on the classification of unlabeled tenants into different security groups, the cloud service provider may modify the VM placement policy to achieve physical isolation among different groups, reducing the co-residency probability between attackers and target tenants. Experiments are conducted on a large-scale dataset collected from Azure Cloud Platform. The results show that the proposed model achieves 97.86% accuracy and an average 96.06% F-score, proving the effectiveness and feasibility of the proposed defense strategy. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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17 pages, 10613 KiB  
Article
Domain-Adaptive Framework for ACL Injury Diagnosis Utilizing Contrastive Learning Techniques
by Weiqiang Liu, Weilun Lin, Zefeng Zhuang and Kehua Miao
Electronics 2024, 13(16), 3211; https://doi.org/10.3390/electronics13163211 - 14 Aug 2024
Viewed by 1059
Abstract
In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques [...] Read more.
In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques for ACL diagnosis work well on single datasets, research on cross-domain data transfer is still lacking. Building strong domain-adaptive diagnostic models requires addressing domain disparities in ACL magnetic resonance imaging (MRI) from different hospitals and making efficient use of multiple ACL datasets. This work uses the publicly available KneeMRI dataset from Croatian hospitals coupled with the publicly available MRnet dataset from Stanford University to investigate domain adaptation and transfer learning models. First, an optimized model efficiently screens training data in the source domain to find unusually misclassified occurrences. Subsequently, before being integrated into the contrastive learning module, a target domain feature extraction module processes features of target domain samples to improve extraction efficiency. By using contrastive learning between positive and negative sample pairs from source and target domains, this method makes domain adaptation easier and improves the efficacy of ACL auxiliary diagnostic models. Utilizing a spatially augmented ResNet-18 backbone network, the suggested approach produces notable enhancements in experimentation. To be more precise, the AUC for transfer learning improved by 3.5% from MRnet to KneeMRI and by 2.5% from KneeMRI to MRnet (from 0.845 to 0.870). This method shows how domain transfer can be used to improve diagnostic accuracy on a variety of datasets and effectively progresses the training of a strong ACL auxiliary diagnostic model. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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18 pages, 2104 KiB  
Article
Light Recurrent Unit: Towards an Interpretable Recurrent Neural Network for Modeling Long-Range Dependency
by Hong Ye, Yibing Zhang, Huizhou Liu, Xuannong Li, Jiaming Chang and Hui Zheng
Electronics 2024, 13(16), 3204; https://doi.org/10.3390/electronics13163204 - 13 Aug 2024
Viewed by 3287
Abstract
Recurrent neural networks (RNNs) play a pivotal role in natural language processing and computer vision. Long short-term memory (LSTM), as one of the most representative RNNs, is built upon relatively complex architecture with an excessive number of parameters, which results in large storage, [...] Read more.
Recurrent neural networks (RNNs) play a pivotal role in natural language processing and computer vision. Long short-term memory (LSTM), as one of the most representative RNNs, is built upon relatively complex architecture with an excessive number of parameters, which results in large storage, high training cost, and lousy interpretability. In this paper, we propose a lightweight network called Light Recurrent Unit (LRU). On the one hand, we designed an accessible gate structure, which has high interpretability and addresses the issue of gradient disappearance. On the other hand, we introduce the Stack Recurrent Cell (SRC) structure to modify the activation function, which not only expedites convergence rates but also enhances the interpretability of the network. Experimental results show that our proposed LRU has the advantages of fewer parameters, strong interpretability, and effective modeling ability for variable length sequences on several datasets. Consequently, LRU could be a promising alternative to traditional RNN models in real-time applications with space or time constraints, potentially reducing storage and training costs while maintaining high performance. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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21 pages, 2479 KiB  
Article
A Data-Driven Pandemic Simulator with Reinforcement Learning
by Yuting Zhang, Biyang Ma, Langcai Cao and Yanyu Liu
Electronics 2024, 13(13), 2531; https://doi.org/10.3390/electronics13132531 - 27 Jun 2024
Viewed by 784
Abstract
After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of [...] Read more.
After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies. Full article
(This article belongs to the Special Issue Data-Driven Intelligence in Autonomous Systems)
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