Submission deadline: CLOSED
Publication: December 2020
Modern systems, such as cooperative robotic systems, mobile computing systems, unmanned aerial vehicles, and financial systems, are becoming distributed, ubiquitous, and systems of systems composed of autonomous entities. They have to operate in highly dynamic and volatile environments, where physical infrastructure, social and societal context, network topologies, and workloads are continuously fluctuating. Consequently, intelligent and autonomous software-intensive behaviors become indispensable characteristics of such systems.
Intelligent Autonomous Systems (IASs) are composed of communicating autonomous components whose behavior may be volatile, i.e., components may break down, become unavailable due to network problems, or change their behavior. Thus, the system has to be intelligent enough to recognize the faulty behavior, adapt itself to new arising situations if possible, and return to its original processing in case the cause of the problem has been removed. Thus, monitoring the system’s environment and adapting the behavior to critical situations is another defining characteristic of IASs. Apart from being aware of their capabilities and limitations, IASs are also capable of reasoning over a diverse body of knowledge. The core of IASs is software shaping the behavior of related industries. Recent advances in the areas of artificial intelligence, machine learning, and deep learning enable autonomous systems with improved robustness and flexibility.
According to various worlds’ leading advisers on business strategy, e.g., McKinsey, Boston Consulting Group, and Roland Berger, billions of dollars will be spent worldwide on IASs in years to come, and the software will be the lynchpin. In the future, using machine vision, motion sensors, image and voice recognition, artificial intelligence, and advanced software, IASs will be able to handle increasingly intelligent work, including interacting with and continuously learning from their environment and especially from people. Where these estimates provide a glimpse of the potential of such systems, they also show many challenges — challenges that may take longer to surmount than enthusiastic early projections suggest.
This theme issue invites papers covering any aspect related to IASs including, but not limited to:
- Robotics and multi-agent systems
- System modeling, analysis, architecture designs, and decisions
- Artificial intelligence, machine learning, and deep learning approaches
- Self-* (self-learning, self-healing, self-organizing, etc.) systems
- Safety and security of autonomous systems
- Case studies, experience reports, benchmarking, and best practices
Submissions should follow the IEEE template and should consist of the following:
- A manuscript of maximum 6,000 words: A PDF of the complete manuscript layout with figures and tables placed within the text. Each figure and table is counted as 300 words.
- A source file in the Word or Latex format.
- High-resolution photos and graphics such as JPEG files are required for the final submission.
- Articles, which have been previously published at a conference, need to have, at least, 30% new material.
- The manuscripts need to be submitted online at https://mc.manuscriptcentral.com/cs-ieee. Select this special-issue option in Step 1 of the submission process to ensure that the article is
reviewed for this special issue.
Contact the guest editors at firstname.lastname@example.org