Proceedings

Improving Multi-agent Coordination by Learning to Estimate Contention

Panayiotis Danassis, Florian Wiedemair, Boi Faltings

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 125-131.

https://doi.org/10.24963/ijcai.2021/18

Keywords

  • Agent-based and Multi-agent Systems: Resource Allocation
  • Agent-based and Multi-agent Systems: Coordination and Cooperation
  • Agent-based and Multi-agent Systems: Multi-agent Learning

Abstract

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (<5% loss) and fair coordination in a variety of synthetic scenarios and a real-world meeting scheduling problem. The lightweight nature and fast learning constitute ALMA-Learning ideal for on-device deployment.

Panayiotis Danassis, Florian Wiedemair, and Boi Faltings. 2021. Improving Multi-agent Coordination by Learning to Estimate Contention. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 125–131. https://doi.org/10.24963/ijcai.2021/18
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