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Journal of Automation and Intelligence is a peer-reviewed journal published by Elsevier, covering Adaptive Control of Nonlinear Systems, Distributed Control Multi-Agent Systems, Stability and Control of Uncertain Systems.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Journal of Automation and Intelligence 12 (2023) 45–58.
Formats any DOI in Journal of Automation and Intelligence style. No sign-up. |
| Publishes research in | Adaptive Control of Nonlinear Systems Distributed Control Multi-Agent Systems Stability and Control of Uncertain Systems Adaptive Dynamic Programming Control Neural Networks Stability and Synchronization |
| ISSN | 2949-8554 |
| Citation impact (2-yr) | 5.11 |
| h-index | 17 |
| i10-index | 27 |
| Total citations | 1,026 |
| Open access | Yes |
| Top institutions publishing here | Chongqing University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works. Then, we review the existing works addressing inherent challenges and those focusing on diverse applications. Some representative stochastic games,…
Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions. However, for embodied tasks, where robots interact with complex environments,…
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during…
Device-free activity recognition plays a crucial role in smart building, security, and human–computer interaction, which shows its strength in its convenience and cost-efficiency. Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models, but it suffers from the limitation of manual feature design. Deep learning overcomes such issues by automatic high-level…
A growing interest in developing autonomous surface vehicles (ASVs) has been witnessed during the past two decades, including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways. This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches. It features a holistic viewpoint of ASV safe navigation,…
