Data-Driven Intelligence in Autonomous Systems

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.

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