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The HoneyComb Paradigm for Research on Collective Human Behavior
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Community-aware task allocation for social networked multiagent systems.

Wanyuan Wang, Yichuan Jiang

    IEEE Transactions on Cybernetics
    |August 20, 2014
    PubMed
    Summary
    This summary is machine-generated.

    We developed a community-aware task allocation model for social networked multiagent systems (SN-MASs). Our heuristic algorithm efficiently maximizes system profit while reducing communication costs in large-scale applications.

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    Area of Science:

    • Artificial Intelligence
    • Multiagent Systems
    • Networked Systems

    Background:

    • Cooperation in social networked multiagent systems (SN-MASs) is often constrained by community structures.
    • Maximizing system profit in these community-aware scenarios is an NP-hard problem.

    Purpose of the Study:

    • To propose a novel community-aware task allocation model for SN-MASs.
    • To develop an effective heuristic algorithm for profit maximization under community constraints.

    Main Methods:

    • A three-phase heuristic algorithm: task selection, community allocation, and agent allocation.
    • Task selection prioritizes desirable tasks.
    • Allocation uses significant task-first and nonoverlap agent-first mechanisms.

    Main Results:

    • The heuristic algorithm closely matches optimal solutions in small-scale applications.
    • In large-scale applications, it achieves similar profit with reduced computational load.
    • The community-aware model lowers communication costs and enhances system profit compared to prior models.

    Conclusions:

    • The proposed community-aware task allocation model and heuristic algorithm are effective for SN-MASs.
    • The approach balances profit maximization with reduced computational and communication overhead.
    • Validated through theoretical analysis and experiments.