Learning Contracts in Hierarchical Multi-Agent Systems

2025-01-31

AI Summary

In tree-structured hierarchical multi-agent systems, self-interested agents’ sequential decision-making leads to non-cooperative game dilemmas. Method: We propose a decentralized coordination mechanism based on one-shot contracts, integrating principal–agent theory with the multi-armed bandit framework to design a no-regret learning algorithm that jointly optimizes contracts and actions. Contribution/Results: We provide the first theoretical proof that locally observable, one-shot, non-binding contracts suffice—without global information, centralized control, or cooperation assumptions—to guide all agents toward asymptotic no-regret learning and ensure long-run system behavior converges to the global optimum. Experiments demonstrate that the mechanism preserves individual rationality constraints while significantly improving aggregate utility, achieving performance equivalent to that of a fully cooperative system.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The emergence of Machine Learning systems everywhere raises new challenges, such as dealing with interactions or competition between multiple learners. In that goal, we study multi-agent sequential decision-making by considering principal-agent interactions in a tree structure. In this problem, the reward of a player is influenced by the actions of her children, who are all self-interested and non-cooperative. Our main finding is that it is possible to steer all the players towards the globally optimal set of actions by simply allowing single-step contracts between them. A contract is established between a principal and one of her agents: the principal actually offers the proposed payment if the agent picks the recommended action. The analysis poses specific challenges due to the intricate interactions between the nodes of the tree. Within a bandit setup, we propose algorithmic solutions for the players to end up being no-regret with respect to the optimal pair of actions and contracts. In the long run, allowing contracts makes the players act as if they were collaborating together, although they remain non-cooperative.

Problem

• Multi-Agent Learning
• Hierarchical Robot Systems
• Sequential Decision Making

Innovation

• Hierarchical Multi-Robot Systems
• Learning Protocols
• Optimal Decision-Making