优化三难困境：去中心化多智能体协调中的效率、舒适度和公平性

中文摘要 AI 辅助

在去中心化环境中进行公平的多智能体协调是构建高效协作系统面临的最紧迫挑战之一。资源分配基于考虑智能体需求的优化集体安排。这种协调不仅要计算高效，还要兼顾公平，即所有智能体产生成本的公平重新分配。近期文献提出了一些算法，能在集中式环境中有效确定平衡系统效率和智能体个体不适的最优计划组合。但这些工作未解决完全去中心化场景下的公平资源优化问题，特别是协调智能体间不适的优化重新分配，以免有智能体不适程度导致激励丧失或极化从而扰乱计划操作。本文研究去中心化多智能体协调中优化三个目标的问题：系统效率、个体舒适度和公平性（即平衡产生的不适成本）。我们设计了一个新颖模型来优化这三个正交目标，且通信和计算开销无大幅增加。通过在两个真实世界数据集上的实验，验证了该模型，表明其能在满足智能体偏好和系统目标的同时实现更公平的优化结果。

英文摘要

The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several algorithms that efficiently determine optimal plan combinations balancing system-wide efficiency and individual discomfort of agents in a centralized setting. However, these works do not address equitable resource optimization in fully decentralized scenarios, specifically, the optimized redistribution of discomfort among coordinating agents so that none experiences a discomfort level that could lead to loss of incentive or polarization that can disrupt planned operations. In this work, we study the problem of optimizing three objectives: (i) system-wide efficiency, (ii) individuals' comfort and (iii) fairness (i.e., balancing of incurred discomfort costs) in decentralized multi-agent coordination. We design a novel model to optimize those three orthogonal objectives, without any substantial increase in communication and computational overhead. Through experiments on two real-world datasets, we validate the model and demonstrate that it can achieve fairer optimization outcomes, while satisfying agents' preferences and system goals.

发表机构

• Google（谷歌）
• School of Energy Systems, LUT University（能源系统学院，卢托大学）

机构由 AI 辅助整理，请以论文原文为准。