Artificial Intelligence Review最新文献

{"title":"Machine learning operations landscape: platforms and tools","authors":"Lisana Berberi,&nbsp;Valentin Kozlov,&nbsp;Giang Nguyen,&nbsp;Judith Sáinz-Pardo Díaz,&nbsp;Amanda Calatrava,&nbsp;Germán Moltó,&nbsp;Viet Tran,&nbsp;Álvaro López García","doi":"10.1007/s10462-025-11164-3","DOIUrl":"10.1007/s10462-025-11164-3","url":null,"abstract":"<div><p>As the field of machine learning advances, managing and monitoring intelligent models in production, also known as machine learning operations (MLOps), has become essential. Organizations are increasingly adopting artificial intelligence as a strategic tool, thus increasing the need for reliable, and scalable MLOps platforms. Consequently, every aspect of the machine learning life cycle, from workflow orchestration to performance monitoring, presents both challenges and opportunities that require sophisticated, flexible, and scalable technological solutions. This research addresses this demand by providing a comprehensive assessment framework of MLOps platforms highlighting the key features necessary for a robust MLOps solution. The paper examines 16 MLOps tools widely used, which revolve around capabilities within AI infrastructure management, including but not limited to experiment tracking, model deployment, and model inference. Our three-step evaluation framework starts with a feature analysis of the MLOps platforms, then GitHub stars growth assessment for adoption and prominence, and finally, a weighted scoring method to single out the most influential platforms. From this process, we derive valuable insights into the essential components of effective MLOps systems and provide a decision-making flowchart that simplifies platform selection. This framework provides hands-on guidance for organizations looking to initiate or enhance their MLOps strategies, whether they require an end-end solutions or specialized tools.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11164-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review","authors":"Shan Lin,&nbsp;Zenglong Liang,&nbsp;Hongwei Guo,&nbsp;Quanke Hu,&nbsp;Xitailang Cao,&nbsp;Hong Zheng","doi":"10.1007/s10462-025-11175-0","DOIUrl":"10.1007/s10462-025-11175-0","url":null,"abstract":"<div><p>Enhancements in monitoring and computational technology have facilitated data accessibility and utilization. Machine learning, as an integral component of the realm of computational technology, is renowned for its universality and efficacy, rendering it pervasive across various domains. Geotechnical disaster early warning systems serve as a crucial safeguard for the preservation of human lives and assets. Machine learning exhibits the capacity to meet the exigencies of prompt and precise disaster prediction, prompting substantial interest in the nexus of these two domains in recent decades. This study accentuates the deployment of machine learning in addressing geotechnical engineering disaster prediction issues through an examination of four types of engineering-specialized research articles spanning the period 2009 to 2024. The study elucidates the evolution and significance of machine learning within the domain of geotechnical engineering disaster prediction, with an emphasis on data analytics and modeling. Addressing the lacunae in existing literature, a user-friendly front-end graphical interface, integrated with machine learning algorithms, is devised to better cater to the requisites of engineering professionals. Furthermore, this research delves into a critical analysis of the prevalent research limitations and puts forth prospective investigational avenues from an applied standpoint.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11175-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning frameworks for MRI-based diagnosis of neurological disorders: a systematic review and meta-analysis","authors":"Syed Saad Azhar Ali,&nbsp;Khuhed Memon,&nbsp;Norashikin Yahya,&nbsp;Shujaat Khan","doi":"10.1007/s10462-025-11146-5","DOIUrl":"10.1007/s10462-025-11146-5","url":null,"abstract":"<div><p>The automatic diagnosis of neurological disorders using Magnetic Resonance Imaging (MRI) is a widely researched problem. MRI is a non-invasive and highly informative imaging modality, which is one of the most widely accepted and used neuroimaging modalities for visualizing the human brain. The advent of tremendous processing capabilities, multi-modal data, and deep-learning techniques has enabled researchers to develop intelligent, sufficiently accurate classification methods. A comprehensive literature review has revealed extensive research on the automatic diagnosis of neurological disorders. However, despite numerous studies, a systematically developed framework is lacking, that relies on a sufficiently robust dataset or ensures reliable accuracy. To date, no consolidated framework has been established to classify multiple diseases and their subtypes effectively based on various types and their planes of orientation in structural and functional MR images. This systematic review provides a detailed and comprehensive analysis of research reported from 2000 to 2023. Systems developed in prior art have been categorized according to their disease diagnosis capabilities. The datasets employed and the tools developed are also summarized to assist researchers to conduct further studies in this crucial domain. The contributions of this research include facilitating the design of a unified framework for multiple neurological disease diagnoses, resulting in the development of a generic assistive tool for hospitals and neurologists to diagnose disorders precisely and swiftly thus potentially saving lives, in addition to increasing the quality of life of patients suffering from neurodegenerative disorders.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11146-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification using hyperdimensional computing: a review with comparative analysis","authors":"Pere Vergés,&nbsp;Mike Heddes,&nbsp;Igor Nunes,&nbsp;Denis Kleyko,&nbsp;Tony Givargis,&nbsp;Alexandru Nicolau","doi":"10.1007/s10462-025-11181-2","DOIUrl":"10.1007/s10462-025-11181-2","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is an emerging and promising paradigm for cognitive computing. At its core, HD/VSA is characterized by its distinctive approach to compositionally representing information using high-dimensional randomized vectors. The recent surge in research within this field gains momentum from its computational efficiency stemming from low-resolution representations and ability to excel in few-shot learning scenarios. Nonetheless, the current literature is missing a comprehensive comparative analysis of various methods since each of them uses a different benchmark to evaluate its performance. This gap obstructs the monitoring of the field’s state-of-the-art advancements and acts as a significant barrier to its overall progress. To address this gap, this review not only offers a conceptual overview of the latest literature but also introduces a comprehensive comparative study of HD/VSA classification methods. The exploration starts with an overview of the strategies proposed to encode information as high-dimensional vectors. These vectors serve as integral components in the construction of classification models. Furthermore, we evaluate diverse classification methods as proposed in the existing literature. This evaluation encompasses techniques such as retraining and regenerative training to augment the model’s performance. To conclude our study, we present a comprehensive empirical study. This study serves as an in-depth analysis, systematically comparing various HD/VSA classification methods using two benchmarks, the first being a set of seven popular datasets used in HD/VSA and the second consisting of 121 datasets being the subset from the UCI Machine Learning repository. To facilitate future research on classification with HD/VSA, we open-sourced the benchmarking and the implementations of the methods we review. Since the considered data are tabular, encodings based on key-value pairs emerge as optimal choices, boasting superior accuracy while maintaining high efficiency. Secondly, iterative adaptive methods demonstrate remarkable efficacy, potentially complemented by a regenerative strategy, depending on the specific problem. Furthermore, we show how HD/VSA is able to generalize while training with a limited number of training instances. Lastly, we demonstrate the robustness of HD/VSA methods by subjecting the model memory to a large number of bit-flips. The results illustrate that the model’s performance remains reasonably stable until the occurrence of 40% of bit flips, where the model’s performance is drastically degraded. Overall, this study performed a thorough performance evaluation on different methods and, on the one hand, a positive trend was observed in terms of improving classification performance but, on the other hand, these developments could often be surpassed by off-the-shelf methods. This calls for better integration with the broader machine learn","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11181-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-model integration for dynamic forecasting (MIDF): a framework for wind speed and direction prediction","authors":"Molaka Maruthi,&nbsp;Bubryur Kim,&nbsp;Song Sujeen,&nbsp;Jinwoo An,&nbsp;Zengshun Chen","doi":"10.1007/s10462-025-11140-x","DOIUrl":"10.1007/s10462-025-11140-x","url":null,"abstract":"<div><p>Accurate forecasting of wind speed and direction is critical for the efficient integration of wind power into energy systems, ensuring reliable renewable energy production and grid stability. Traditional methods often struggle with capturing nonlinear interdependencies, quantifying uncertainties, and providing reliable long-term predictions, particularly in complex atmospheric conditions. To address these challenges, this study introduces multi-model Integration for dynamic forecasting (MIDF), an ensemble machine learning framework that combines the strengths of DeepAR and temporal fusion transformer (TFT) models through a two-step meta-learning process. MIDF leverages DeepAR’s probabilistic forecasting capabilities and TFT’s attention mechanisms to enhance accuracy, robustness, and interpretability. Using a custom meteorological dataset spanning January 2010 to May 2023, the model was evaluated against standalone alternatives across multiple metrics, including MSE, RMSE, and R². MIDF achieved superior performance, with MSE, RMSE, and R² values of 0.0035, 0.01913, and 0.89 for wind speed, and 0.00052, 0.02507, and 0.86 for wind direction, significantly reducing errors compared to existing methods. These results underscore the potential of ensemble learning in advancing wind forecasting accuracy, enabling more reliable renewable energy management, operational planning, and risk mitigation in meteorological applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11140-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of small object detection based on deep learning in aerial images","authors":"Wei Hua,&nbsp;Qili Chen","doi":"10.1007/s10462-025-11150-9","DOIUrl":"10.1007/s10462-025-11150-9","url":null,"abstract":"<div><p>Small object detection poses a formidable challenge in the field of computer vision, particularly when it comes to analyzing aerial remote sensing images. Despite the rapid development of deep learning and significant progress in detection techniques in natural scenes, the migration of these algorithms to aerial images has not met expectations. This is primarily due to limitations in imaging acquisition conditions, including small target size, viewpoint specificity, background complexity, as well as scale and orientation diversity. Although the increasing application of deep learning-based algorithms to overcome these problems, few studies have summarized the optimization of different deep learning strategies used for small target detection in aerial images. Therefore, this paper aims to explore the application of deep learning methods for small object detection in aerial images. The primary challenges in small object detection in aerial images will be summarized. Next, a meticulous analysis and categorization of the prevailing deep learning optimization strategies employed to surmount the challenges encountered in aerial image detection is undertaken. Following that, we provide a comprehensive presentation of the object detection datasets utilized in aerial remote sensing images, along with the evaluation metrics employed. Additionally, we furnish experimental data pertaining to the currently proposed detection algorithms. Finally, the advantages and disadvantages of various optimization strategies and potential development trends are discussed. Hopefully, it can provide a reference for researchers in this field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11150-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BERT applications in natural language processing: a review","authors":"Nadia Mushtaq Gardazi,&nbsp;Ali Daud,&nbsp;Muhammad Kamran Malik,&nbsp;Amal Bukhari,&nbsp;Tariq Alsahfi,&nbsp;Bader Alshemaimri","doi":"10.1007/s10462-025-11162-5","DOIUrl":"10.1007/s10462-025-11162-5","url":null,"abstract":"<div><p>BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, including its structure, utilization in different NLP tasks, and the further development of its design via modifications. The study thoroughly analyses the methodological aspects, conducting a comprehensive analysis of the planning process, the implemented procedures, and the criteria used to decide which data to include or exclude in the evaluation framework. In addition, the study thoroughly examines the influence of BERT on several NLP tasks, such as Sentence Boundary Detection, Tokenization, Grammatical Error Detection and Correction, Dependency Parsing, Named Entity Recognition, Part of Speech Tagging, Question Answering Systems, Machine Translation, Sentiment analysis, fake review detection and Cross-lingual transfer learning. The review study adds to the current literature by integrating ideas from multiple sources, explicitly emphasizing the problems and prospects in BERT-based models. The objective is to comprehensively comprehend BERT and its implementations, targeting both experienced researchers and novices in the domain of NLP. Consequently, the present study is expected to inspire more research endeavors, promote innovative adaptations of BERT, and deepen comprehension of its extensive capabilities in various NLP applications. The results presented in this research are anticipated to influence the advancement of future language models and add to the ongoing discourse on enhancing technology for understanding natural language.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11162-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent games meeting with multi-agent deep reinforcement learning: a comprehensive review","authors":"Yiqin Wang,&nbsp;Yufeng Wang,&nbsp;Feng Tian,&nbsp;Jianhua Ma,&nbsp;Qun Jin","doi":"10.1007/s10462-025-11166-1","DOIUrl":"10.1007/s10462-025-11166-1","url":null,"abstract":"<div><p>Recent years have witnessed the great achievement of the AI-driven intelligent games, such as AlphaStar defeating the human experts, and numerous intelligent games have come into the public view. Essentially, deep reinforcement learning (DRL), especially multiple-agent DRL (MADRL) has empowered a variety of artificial intelligence fields, including intelligent games. However, there is lack of systematical review on their correlations. This article provides a holistic picture on smoothly connecting intelligent games with MADRL from two perspectives: theoretical game concepts for MADRL, and MADRL for intelligent games. From the first perspective, information structure and game environmental features for MADRL algorithms are summarized; and from the second viewpoint, the challenges in intelligent games are investigated, and the existing MADRL solutions are correspondingly explored. Furthermore, the state-of-the-art (SOTA) MADRL algorithms for intelligent games are systematically categorized, especially from the perspective of credit assignment. Moreover, a comprehensively review on notorious benchmarks are conducted to facilitate the design and test of MADRL based intelligent games. Besides, a general procedure of MADRL simulations is offered. Finally, the key challenges in integrating intelligent games with MADRL, and potential future research directions are highlighted. This survey hopes to provide a thoughtful insight of developing intelligent games with the assistance of MADRL solutions and algorithms.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11166-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GFSNet: Gaussian Fourier with sparse attention network for visual question answering","authors":"Xiang Shen,&nbsp;Dezhi Han,&nbsp;Chin-Chen Chang,&nbsp;Ammar Oad,&nbsp;Huafeng Wu","doi":"10.1007/s10462-025-11163-4","DOIUrl":"10.1007/s10462-025-11163-4","url":null,"abstract":"<div><p>Visual question answering (VQA), a core task in multimodal learning, requires models to effectively integrate visual and natural language information to perform reasoning and semantic understanding in complex scenarios. However, self-attention mechanisms often struggle to capture multi-scale information and key region features within images comprehensively. Moreover, VQA involves multidimensional and deep reasoning about image content, particularly in scenarios involving spatial relationships and frequency-domain features. Existing methods face limitations in modeling multi-scale features and filtering irrelevant information effectively. This paper proposes an innovative Gaussian Fourier with Sparse Attention Network (GFSNet) to address these challenges. GFSNet leverages Fourier transforms to map image attention weights generated by the self-attention mechanism from the spatial domain to the frequency domain, enabling comprehensive modeling of multi-scale frequency information. This enhances the model’s adaptability to complex structures and its capacity for relational modeling. To further improve feature robustness, a Gaussian filter is introduced to suppress high-frequency noise in the frequency domain, preserving critical visual information. Additionally, a sparse attention mechanism dynamically selects optimized frequency-domain features, effectively reducing interference from redundant information while improving interpretability and computational efficiency. Without increasing parameter counts or computational complexity, GFSNet achieves efficient modeling of multi-scale visual information. Experimental results on benchmark VQA datasets (VQA v2, GQA, and CLEVR) demonstrate that GFSNet significantly enhances reasoning capabilities and cross-modal alignment performance, validating its superiority and effectiveness. The code is available at https://github.com/shenxiang-vqa/GFSNet.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11163-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive gaining-sharing knowledge-based variant algorithm with historical probability expansion and its application in escape maneuver decision making","authors":"Lei Xie,&nbsp;Yuan Wang,&nbsp;Shangqin Tang,&nbsp;Yintong Li,&nbsp;Zhuoran Zhang,&nbsp;Changqiang Huang","doi":"10.1007/s10462-024-11096-4","DOIUrl":"10.1007/s10462-024-11096-4","url":null,"abstract":"<div><p>To further improve the performance of adaptive gaining-sharing knowledge-based algorithm (AGSK), a novel adaptive gaining sharing knowledge-based algorithm with historical probability expansion (HPE-AGSK) is proposed by modifying the search strategies. Based on AGSK, three improvement strategies are proposed. First, expansion sharing strategy is proposed and added in junior gaining-sharing phase to boost local search ability. Second, historical probability expansion strategy is proposed and added in senior gaining-sharing phase to strengthen global search ability. Last, reverse gaining strategy is proposed and utilized to expand population distribution at the beginning of iterations. The performance of HPE-AGSK is initially evaluated using IEEE CEC 2021 test suite, compared with fifteen state-of-the-art algorithms (AGSK, APGSK, APGSK-IMODE, GLAGSK, EDA2, AAVS-EDA, EBOwithCMAR, LSHADE-SPACMA, HSES, IMODE, MadDE, CJADE, and iLSHADE-RSP). The results demonstrate that HPE-AGSK outperforms both state-of-the-art GSK-based variants and past winners of IEEE CEC competitions. Subsequently, GSK-based variants and other exceptional algorithms in CEC 2021 are selected to further evaluate the performance of HPE-AGSK using IEEE CEC 2018 test suite. The statistical results show that HPE-AGSK has superior exploration ability than the comparison algorithms, and has strong competition with APGSK (state-of-the-art AGSK variant) and IMODE (CEC 2020 Winner) in exploitation ability. Finally, HPE-AGSK is utilized to solve the beyond visual range escape maneuver decision making problem. Its success rate is 100%, and mean maneuver time is 9.10 s, these results show that HPE-AGSK has good BVR escape maneuver decision-making performance. In conclusion, HPE-AGSK is a highly promising AGSK variant that significantly enhances the performance, and is an outstanding development of AGSK. The code of HPE-AGSK can be downloaded from https://github.com/xieleilei0305/HPE-AGSK-CODE.git. (The link will be available for readers after the paper is published).</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11096-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}