Collaborative Intelligence for Connected Agents

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1570

Editors


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Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: multi-agent systems; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: internet of vehicles; edge intelligence; vehicular networking

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Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: multimodal perception; multi-agent collaboration; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous advancement of intelligent and connected technologies, multi-agent collaboration has evolved into a core paradigm for shaping the intelligent societies of the future. Historically, close human collaboration has driven leaps in productivity; similarly, the transition of machine intelligence from “single-agent intelligence” to “collaborative intelligence” signals a fundamental transformation in system capabilities and efficiency. This shift builds upon breakthroughs in perception, cognition, and decision-making at the single-agent level, providing a solid foundation for large-scale collective perception, distributed reasoning, and autonomous collaborative decision-making. Against this backdrop, multi-agent collaborative technologies are expanding their transformative application potential across diverse domains.

However, transitioning from independently operating intelligent agents to highly autonomous collaborative groups remains a systemic challenge. This difficulty primarily revolves around three key dimensions: collaborative perception, collaborative cognition, and collaborative decision-making, representing the main bottlenecks that constrain multi-agent systems from evolving toward higher-order, more generalizable collective intelligence paradigms. Addressing these challenges systematically is not merely a matter of incremental technical improvement; it is the essential pathway for advancing the field from “single-agent intelligence” to “collective intelligence.”

This Special Issue welcomes high-quality submissions on multi-agent collaborative intelligence, with a focus on collective perception, cognition, and decision-making. Contributions addressing theoretical, algorithmic, and system-level aspects are encouraged. Topics of interest include, but are not limited to, the following:

  • Collaborative perception and sensor fusion for multi-agent systems;
  • Collaborative cognitive modeling and shared world representations;
  • Multi-agent decision-making, task allocation, and coordination strategies;
  • Communication-efficient coordination and information sharing among agents;
  • Collaborative localization, mapping, positioning, and navigation in multi-agent networks;
  • Edge–cloud and hybrid computing architectures supporting multi-agent collaboration;
  • Privacy, security, and trustworthiness in multi-agent systems;
  • Human–robot or human–AI collaborative intelligence;
  • Swarm intelligence and large-scale autonomous agent coordination;
  • Adaptive planning and resource management for dynamic multi-agent scenarios;
  • Benchmark datasets, evaluation metrics, and reproducible research for collaborative multi-agent systems.

Dr. Guiyang Luo
Prof. Dr. Kaige Qu
Dr. Hui Zhang
Guest Editors

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Keywords

  • multi-agent systems
  • collaborative perception
  • multi-agent decision-making
  • collaborative localization
  • collaborative navigation
  • collaborative intelligence

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Published Papers (1 paper)

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Research

18 pages, 1843 KB  
Article
Heterogeneous Computing Resources Scheduling Based on Time-Varying Graphs and Multi-Agent Reinforcement Learning
by Jinshan Yuan, Xuncai Zhang and Kexin Gong
Future Internet 2026, 18(3), 168; https://doi.org/10.3390/fi18030168 - 20 Mar 2026
Viewed by 1196
Abstract
The evolution toward 6G Computing Power Networks (CPN) aims to deeply integrate multi-tier computing resources across Cloud, Edge, and end devices. However, the significant heterogeneity of computing resources, characterized by varying hardware architectures such as CPUs, GPUs, and NPUs, coupled with the time-varying [...] Read more.
The evolution toward 6G Computing Power Networks (CPN) aims to deeply integrate multi-tier computing resources across Cloud, Edge, and end devices. However, the significant heterogeneity of computing resources, characterized by varying hardware architectures such as CPUs, GPUs, and NPUs, coupled with the time-varying network topology caused by terminal mobility, poses severe challenges to realizing efficient integrated scheduling that satisfies Quality of Service (QoS). To address spatiotemporal mismatches between task requirements and hardware architectures, this paper proposes an integrated scheduling method combining Discrete Time-Varying Graph (DTVG) construction with Multi-Agent Reinforcement Learning (MARL). Specifically, we model the dynamic interaction between mobile tasks and heterogeneous nodes as a DTVG to capture spatiotemporal evolution and employ a QMIX-based algorithm to enable collaborative decision-making among distributed agents. Simulation results demonstrate that the proposed approach effectively solves the joint optimization problem of heterogeneous resource matching and dynamic path planning, significantly outperforming traditional baselines in terms of resource utilization and average latency. This study confirms that incorporating graph-theoretic modeling with reinforcement learning offers a robust solution for the complex coupling of communication and computation in dynamic 6G networks. Full article
(This article belongs to the Special Issue Collaborative Intelligence for Connected Agents)
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