Decentralized adaptive task allocation for dynamic multi-agent systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a decentralized system for dynamic task assignment in multi-agent systems, enabling efficient, scalable, and robust online allocation without central control.
Area Of Science
- Artificial Intelligence
- Robotics
- Distributed Systems
Background
- Multi-agent systems require efficient dynamic task assignment strategies.
- Existing methods often struggle with partial observability, noisy feedback, and limited communication.
Purpose Of The Study
- To develop a decentralized two-layer architecture for dynamic task assignment.
- To enable scalable, online task allocation without centralized coordination.
- To ensure robustness under challenging operational conditions.
Main Methods
- Adaptive controllers using recursive regression with forgetting for parameter prediction.
- Selective task broadcasting based on relevance and agent availability.
- Distributed optimization combining Simultaneous Perturbation Stochastic Approximation (SPSA) and consensus-based synchronization.
Main Results
- Demonstrated scalability and online task allocation capabilities.
- Showcased robustness across varying noise levels, task dynamics, and input arrival patterns.
- Successfully applied to prompt-based task assignment for large language models (LLMs).
Conclusions
- The proposed decentralized architecture effectively addresses dynamic task assignment in multi-agent systems.
- The system provides a robust and scalable solution for real-world applications.
- This approach facilitates efficient coordination in complex, partially observable environments.
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