AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS

Scope & Guideline

Charting New Frontiers in Multi-Agent Systems

Introduction

Welcome to the AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1387-2532
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1998 to 2024
AbbreviationAUTON AGENT MULTI-AG / Auton. Agents Multi-Agent Syst.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Aims and Scopes

The journal "AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS" focuses on the theoretical and practical aspects of autonomous agents and multi-agent systems, emphasizing their design, implementation, and applications in various domains. The research published in this journal spans a wide range of methodologies, including computational modeling, algorithm design, and empirical evaluations.
  1. Multi-Agent System Design:
    Explores the principles and methodologies for designing effective multi-agent systems, including agent interaction, negotiation, and cooperation.
  2. Reinforcement Learning in Multi-Agent Contexts:
    Focuses on the application of reinforcement learning techniques within multi-agent settings, addressing challenges like coordination, communication, and shared learning.
  3. Mechanism Design and Game Theory:
    Investigates game-theoretic approaches and mechanism design principles to analyze and create systems that promote desired outcomes in agent interactions.
  4. Human-Agent Interaction:
    Studies the dynamics of interactions between humans and autonomous agents, including trust, communication, and the integration of human feedback into agent decision-making.
  5. Social and Ethical Implications of Agents:
    Addresses the ethical considerations and social impact of deploying autonomous agents, including fairness, accountability, and transparency in their decision-making processes.
The journal has seen significant growth in certain areas, reflecting the current trends and emerging themes in the field of autonomous agents and multi-agent systems. These themes highlight innovative approaches and applications that are gaining traction among researchers.
  1. Deep Reinforcement Learning Applications:
    There is a marked increase in research applying deep reinforcement learning techniques to complex multi-agent environments, showcasing advancements in scalability and performance.
  2. Trust and Ethical AI:
    Emerging themes around trustworthiness and ethical considerations in AI and agent interactions are gaining attention, with studies focusing on transparency and accountability in decision-making.
  3. Collaborative and Cooperative Systems:
    The trend towards developing systems that promote cooperation among agents is on the rise, with an emphasis on emergent behaviors and collective decision-making processes.
  4. Interdisciplinary Approaches:
    Research increasingly incorporates insights from social sciences, economics, and psychology to enhance the design and functionality of multi-agent systems, reflecting a broader understanding of agent interactions.
  5. Adaptive and Context-Aware Agents:
    The development of agents that can adapt to changing environments and contexts is a growing focus, highlighting the importance of flexibility and responsiveness in autonomous systems.

Declining or Waning

While the journal has consistently focused on core areas of autonomous agents and multi-agent systems, certain themes appear to be declining in prominence as research priorities evolve. The following points highlight these waning scopes.
  1. Static Game Theoretical Models:
    There has been a noticeable decrease in publications focusing on static models of game theory, as the field shifts towards dynamic and adaptive approaches that better reflect real-world complexities.
  2. Basic Algorithmic Strategies:
    Research focusing solely on traditional algorithmic strategies without considering their application in complex environments or interactions has diminished, indicating a move towards more integrated approaches.
  3. Single-Agent Systems:
    The focus on single-agent systems has waned, as the community increasingly recognizes the importance of multi-agent interactions and their implications for system performance and behavior.

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