Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?

Stephen Mak, Tim Pearce, Matthew Macfarlane, Liming Xu, Michael Ostroumov, Alexandra Brintrup

Other Workshops: Tackling Climate Change with Machine Learning; Algorithmic Fairness through the Lens of Time

Keywords: Cooperative AI, Trustworthy AI, Cooperative Logistics, Reinforcement Learning, Coalition Structure Generation, Cooperative Game Theory

TL;DR: We propose deep reinforcement learning for coalition structure generation. We introduce computational challenges of cooperative logistics, how AI can help and vice versa.

Abstract: Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO2. Whilst well-studied in operations research – industrial adoption remains limited due to a lack of trustworthy cooperation. A key remaining challenge is fair and scalable gain sharing (i.e., how much should each company be fairly paid?). This paper introduces the novel algorithmic challenges that Cooperative Logistics offers AI, and novel applications of AI towards Cooperative Logistics. We further present findings from our initial experiments.

Submission Number: 14