ACM Transactions on Autonomous and Adaptive Systems

Scope & Guideline

Navigating the Frontiers of Autonomous and Adaptive Research.

Introduction

Delve into the academic richness of ACM Transactions on Autonomous and Adaptive Systems with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1556-4665
PublisherASSOC COMPUTING MACHINERY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2006 to 2024
AbbreviationACM T AUTON ADAP SYS / ACM Trans. Auton. Adapt. Syst.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434

Aims and Scopes

The ACM Transactions on Autonomous and Adaptive Systems (TAAS) focuses on the development of systems that can autonomously adapt to changing environments and requirements. The journal encompasses a broad range of methodologies and applications aimed at enhancing the adaptability and autonomy of systems in various domains.
  1. Autonomous Systems Development:
    The journal emphasizes research on the design and implementation of autonomous systems that can operate independently and make decisions based on environmental inputs.
  2. Self-Adaptation Mechanisms:
    A significant focus is placed on self-adaptive mechanisms that allow systems to modify their behavior or structure in response to changing requirements or conditions.
  3. Machine Learning Integration:
    The incorporation of machine learning techniques into adaptive systems is a core area, enabling systems to learn from data and improve their performance over time.
  4. Cyber-Physical Systems:
    Research related to the integration of physical processes with computational systems is highlighted, particularly in ensuring resilience and efficiency in applications like IoT and robotics.
  5. Multi-Agent Systems:
    The journal explores frameworks and strategies for managing interactions and cooperation among multiple autonomous agents, enhancing their collective performance.
  6. Frameworks for Adaptation:
    The development of theoretical and practical frameworks to facilitate adaptation processes in various contexts, including software, networks, and robotics.
The ACM Transactions on Autonomous and Adaptive Systems has exhibited a notable shift towards several emerging themes in recent publications. These themes reflect the evolving landscape of technology and the increasing complexity of adaptive systems.
  1. Lifelong Learning and Adaptation:
    Recent papers emphasize lifelong learning paradigms in adaptive systems, highlighting the importance of systems that continuously learn and adapt beyond initial training phases.
  2. Human-Machine Collaboration:
    There is a growing focus on frameworks that facilitate effective human-machine collaboration, particularly in environments where autonomous systems operate alongside human operators.
  3. Edge Computing and Resource Management:
    Emerging research explores the management of resources in edge computing environments, particularly concerning the adaptation of services and applications in response to local conditions.
  4. Security and Resilience in Adaptive Systems:
    A trend towards ensuring security and resilience in adaptive systems has emerged, addressing the challenges posed by cyber threats and the need for robust operation in critical applications.
  5. Sustainability in Adaptive Systems:
    Research focusing on sustainable practices within autonomous systems, particularly in networked and resource-constrained environments, is increasingly prevalent, reflecting broader societal concerns.

Declining or Waning

In recent years, certain research themes within the ACM Transactions on Autonomous and Adaptive Systems appear to be losing prominence. This decline may reflect shifts in technological focus or changes in research priorities among authors.
  1. Static Adaptation Models:
    Research focusing on static or rigid adaptation models has decreased, as the field moves towards more dynamic and flexible approaches that can respond to real-time changes.
  2. Basic Reinforcement Learning Applications:
    While reinforcement learning remains crucial, papers focusing solely on basic applications without integration into broader adaptive frameworks have become less frequent.
  3. Traditional Control Systems:
    The application of traditional control systems in autonomous contexts has waned, as newer methodologies that leverage AI and machine learning take precedence.
  4. Single-Agent Focus:
    There has been a decline in research concentrating on single-agent systems, with a noticeable shift towards multi-agent approaches that emphasize collaboration and collective intelligence.
  5. General Surveys without Novel Contributions:
    Surveys that do not present new insights or frameworks are appearing less frequently, indicating a trend towards more innovative and application-specific research.

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