Evaluating Emergent Coordination in Multi-Agent Task Allocation Through Causal Inference and Sub-Team Identification

Haochen Wu, Amin Ghadami, Alparslan Emrah Bayrak, Jonathon M. Smereka, Bogdan I. Epureanu

    Research output: Contribution to journalArticlepeer-review

    5 Scopus citations

    Abstract

    Coordination in multi-agent systems is a vital component in teaming effectiveness. In dynamically changing situations, agent decisions depict emergent coordination strategies from following pre-defined rules to exploiting incentive-driven policies. While multi-agent reinforcement learning shapes team behaviors from experience, interpreting learned coordination strategies offers benefits in understanding complex agent dynamics and further improvement in developing adaptive strategies for evolving and unexpected situations. In this work, we develop an approach to quantitatively measure team coordination by collecting decision time series data, detecting causality between agents, and identifying statistically high coordinated sub-teams in decentralized multi-agent task allocation operations. We focus on multi-agent systems with homogeneous agents and homogeneous tasks as the strategy formation is more ambiguous and challenging than heterogeneous teams with specialized capabilities. Emergent team coordination is then analyzed using rule-based and reinforcement learning-based strategies for task allocation in operations at different demand stages (stress) levels. We also investigate correlation vs. causation and agent over- or under-estimating demand levels.

    Original languageEnglish
    Pages (from-to)728-735
    Number of pages8
    JournalIEEE Robotics and Automation Letters
    Volume8
    Issue number2
    DOIs
    StatePublished - 1 Feb 2023

    Keywords

    • Multi-Robot Systems
    • Planning
    • Reinforcement Learning
    • Scheduling and Coordination
    • Task Planning

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