arXiv cs.MA· Nikolaos Al. Papadopoulos, Ismael Tito Freire, Marti Sanchez-Fibla, Konstantinos E. Psannis·· 12 小时前AI 评分26
多智能体系统中的时间公平分配：从精确交替指标到可扩展协调代理指标
Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies
AI 导读
研究提出 Rotational Periodicity 指标族，通过衡量成功访问间等待时间的规律性与各智能体访问频率的均衡度，来区分有序轮流与不规则访问模式。在 2 至 10 个强化学习智能体竞争的 Honey-Jar Game 中，独立训练的 Q-learning 智能体协调表现常显著差于随机策略智能体，而传统公平指标却一致报告良好结果。
正文 · 原文
Abstract:Many multi-agent games and simulated environments involve several independent, often learning, agents repeatedly competing for a shared resource. Conventional fairness measures assess whether resources are shared equally overall, but cannot distinguish orderly turn-taking from irregular access patterns that produce long, unpredictable waiting times despite similar cumulative outcomes. We introduce Rotational Periodicity, a family of computationally efficient metrics combining two complementary measurements, the regularity of waiting times between successful accesses and the balance of access frequencies across agents. We evaluate them against a more detailed family of alternation metrics in a repeated threshold-congestion game, the Honey-Jar Game, in which two to ten reinforcement-learning agents compete for exclusive access to a shared high-value resource. Our experiments show that independently trained Q-learning agents often coordinate substantially worse than random-policy agents, even though conventional fairness metrics consistently report favourable outcomes. Every member of the family closely reproduces the rankings of the more computationally expensive alternation metrics, while computing six to twenty times faster as the number of agents grows. A direct timing study, which separates the two quantities that drive that cost, measures the advantage growing in proportion to the number of agents out to two thousand agents. These findings show that evaluating multi-agent learning systems requires temporally aware measures of coordination, not only aggregate outcomes, and that efficient proxies make this practical for larger multi-a
| Comments: | 18 pages, 6 figures, 11 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Subjects: | Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) |
| MSC classes: | 91A20, 91B32, 68T42 |
| ACM classes: | I.2.11; F.2.2; J.4 |
| Cite as: | arXiv:2605.14879 [cs.MA] |
| (or arXiv:2605.14879v3 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14879 arXiv-issued DOI via DataCite |
Submission history
From: Nikolaos Papadopoulos [view email]
[v1]
Thu, 14 May 2026 14:23:32 UTC (643 KB)
[v2]
Tue, 21 Jul 2026 22:12:22 UTC (1,263 KB)
[v3]
Fri, 23 Oct 2026 14:34:54 UTC (916 KB)
来源：arXiv cs.MA · arxiv.org