ACM Transactions on Autonomous and Adaptive Systems (TAAS) is a venue for high-quality research contributions addressing foundational, engineering, and technological aspects supporting the development and management of autonomous adaptive systems. TAAS focuses on system design, system software architectures, and enabling technologies. It particularly welcomes results supported by experimental validation.

Scope of the Journal

TAAS aims at disseminating high quality scientific and technological research results in the area of autonomous and adaptive systems, as defined in the About section. It is published on a quarterly basis.

Types of Articles

TAAS aims to publish papers that provably advance the state of the art, or that provide new insights and knowledge into specific issues related to autonomous and adaptive systems.

Authors submitting papers to this journal should support their arguments via performance evaluations, logical proofs, and statistical data or usability tests - depending on what applies to the specific case. Also, authors must demonstrate novelty with respect to the state of the art.

Survey papers are welcome, provided that they are of high quality, and may serve the purpose of informing researchers, professionals, and students about areas within the remit of this journal.

Purely speculative papers, papers that claim research results without supporting them with appropriate validation arguments, or survey papers that reduce to a list of known techniques without apparent rationale are not of interest to this journal.

Originality

Articles submitted to TAAS must be original, and must not have been published (or been/being submitted for evaluation) elsewhere. Extended versions of papers from conference and workshop proceedings may be acceptable, provided that the TAAS version notably extends prior publications with new results and discussion. Authors are required to clearly state where this is the case when they submit their paper to TAAS.

Style and Length

Articles must be in English. They should have an introductory part that is comprehensible by a non-expert, and should reference up-to-date related literature. TAAS enforces a limit on the length of papers to 10000 words (20 printed pages). Papers exceeding 10000 words will have to either be shortened, or have to move some of the material to an appendix that will only be published online.

Unethical articles

TAAS reserves the right to impose the revision of, or if necessary to refuse, a paper - despite its scientific and technical quality - whenever its content is explicitly unethical or if it supports racism, sexual or religious discrimination, illegal activities or terrorism; similarly an article may be refused if the editors deem that it might harm the political or religious sensitivity of interested readers in any manner.

Special Issue on Autonomous and Adaptive Systems for Sustainable Marine Ecosystem
Día de Entrega: 2024-11-30

Guest Editors:
• Jiachen Yang, Tianjin University, yangjiachen@tju.edu.cn
• Qinggang Meng, Loughborough University, Q.Meng@lboro.ac.uk
• Houbing Herbert Song, University of Maryland, Baltimore County (UMBC), h.song@ieee.org

Background:
The ocean, covering more than 70% of the Earth's surface, is a vast repository of biodiversity, geological resources,
and data. However, the scale and complexity of marine environments pose significant challenges to exploration and
resource management, exacerbated by harsh conditions and accessibility issues. The incorporation of Artificial
Intelligence (AI) and autonomous and adaptive systems into sustainable marine ecosystems is vital for unlocking
the potential of our oceans. Autonomous and adaptive systems are at the cutting edge of oceanographic research,
providing unparalleled capabilities for data-driven insights and automated operations within marine settings.
The urgency for AI-enabled marine ecosystems is underscored by the swiftly altering marine landscapes due to
climate change, overfishing, and pollution. These adaptive systems are crucial for real-time monitoring, predictive
analytics, and the sustainable exploitation of marine resources. Utilizing these technologies is pivotal for advancing
marine sciences, ensuring environmental stewardship, and driving the growth of the blue economy. In particular,
the capabilities of autonomous and adaptive systems to manage dynamic environments and respond to ever-
changing ecological conditions are key to achieving sustainability in marine ecosystems. This special issue aims to
highlight innovative contributions that underscore the significance of AI and autonomous systems in addressing the
multifaceted challenges of marine exploration, conservation, and resource management.

Scope:

This special issue is dedicated to pioneering research in the deployment of autonomous and adaptive systems aimed
at ensuring the sustainability of marine ecosystems. We are seeking high-quality research that showcases
innovative AI methods, algorithms, and practical solutions that contribute to maintaining and enhancing the health
of marine environments.

Submissions should emphasize the integration of AI with marine technologies to advance data collection,
processing, and the generation of insights that can inform sustainable practices. We are looking for papers that
illustrate how technological innovations lead to resilient autonomous systems adept at performing sophisticated
tasks such as ecosystem health monitoring, predictive modeling for marine biodiversity, and the sustainable
management of marine resources. Moreover, we are interested in research that delves into the ethical and
environmental considerations of employing AI and autonomous systems in the effort to preserve and protect our
oceans.

Given the interdisciplinary nature of sustainable marine ecosystem management, we welcome contributions from
a wide range of fields including marine science, computer science, robotics, environmental science, and others.

Topics

Topics to be discussed in this special issue include (but are not limited to) the following:
• Autonomous Algorithms for Oceanic Climate Impact Assessment
• AI-Driven Monitoring and Conservation Strategies for Marine Biodiversity
• Advanced Data Analysis Techniques for Marine Sensor Networks
• Intelligent Navigation Systems for Marine Autonomous Vehicles
• Intelligent data management and processing for marine geographic information system
• Adaptive Sampling Strategies in Marine Research via Reinforcement Learning
• Digital Twins solutions in support of the design and runtime operations of autonomous and adaptive marine
ecosystems
• Real-Time Decision Support Systems for Marine Disaster Response
• Ethical Considerations in the Use of AI for Marine Resource Exploitation
• Innovative and scalable dynamic data-driven models, design, architectures, and development methods
addressing challenges in engineering autonomous and self-adaptive systems, including self-* and control
modalities with human-AI teaming.
• Rigorous testing, verification and model checking methods for autonomous and self-adaptive systems
supporting monitoring, analysis, planning, dynamic feedback control and knowledge management of
sustainable marine ecosystem.
• Futuristic design, architectures and hypothetical studies supported by rigorous theoretical formulation,
experimentation, and proof-of-concepts.

Important Dates
• Submissions deadline: November 30, 2024
• First-round review decisions: December 31, 2024
• Deadline for revision submissions: January 31, 2025
• Notification of final decisions: March 31, 2025
• Tentative publication: April 30, 2025

Submission Information
The special issue calls for high-quality research contributions addressing foundational, engineering, and
technological aspects supporting the development, management, control, and evolution of long-lived autonomous
and adaptive systems of sustainable marine ecosystems. We invite high‐quality submissions, covering fundamental
research, experience reports, case studies, and futuristic applications supported by rigorous experimentation. Guest
editors will prescreen submitted manuscripts for suitability. Submissions passing the prescreen process will go
through a rigorous peer-review process according to the standards of ACM TAAS.
For questions and further information, please contact Jiachen Yang/yangjiachen@tju.edu.cn.