Cinder: 一个快速且公平的配对系统
Cinder: A fast and fair matchmaking system
AI总结：
Cinder通过两阶段配对系统，利用Ruzicka相似性指数和Kantorovich距离，实现快速且公平的多人在线游戏配对。
摘要：
一个公平且快速的配对系统是现代多人在线游戏的重要组成部分，直接影响玩家的留存率和满意度。然而，将异质技能水平的副本（预组成队伍）进行公平配对是一个重大挑战。仅基于平均队伍技能指标（如均值或中位数评分或排名）进行配对，往往会导致不平衡且单方面的游戏，尤其是在技能分布较宽或偏斜时。本文介绍了Cinder，一个两阶段配对系统，旨在提供快速且公平的配对。Cinder首先通过Ruzicka相似性指数比较副本的“非异常”技能范围进行快速初步过滤。通过此初始检查的副本随后会使用更精确的公平性度量进行评估。第二阶段涉及将玩家排名映射到一个非线性的技能桶集合，该集合由反向正态分布生成，以在平均技能水平提供更高的粒度。然后通过Kantorovich距离对副本的排序桶索引量化潜在配对的公平性，产生一个“制裁分数”。我们通过分析1.4亿次模拟副本配对的制裁分数分布来证明该系统的可行性，为公平配对阈值提供了一个稳健的基础。
英文摘要：
A fair and fast matchmaking system is an important component of modern multiplayer online games, directly impacting player retention and satisfaction. However, creating fair matches between lobbies (pre-made teams) of heterogeneous skill levels presents a significant challenge. Matching based simply on average team skill metrics, such as mean or median rating or rank, often results in unbalanced and one-sided games, particularly when skill distributions are wide or skewed. This paper introduces Cinder, a two-stage matchmaking system designed to provide fast and fair matches. Cinder first employs a rapid preliminary filter by comparing the "non-outlier" skill range of lobbies using the Ruzicka similarity index. Lobbies that pass this initial check are then evaluated using a more precise fairness metric. This second stage involves mapping player ranks to a non-linear set of skill buckets, generated from an inverted normal distribution, to provide higher granularity at average skill levels. The fairness of a potential match is then quantified using the Kantorovich distance on the lobbies' sorted bucket indices, producing a "Sanction Score." We demonstrate the system's viability by analyzing the distribution of Sanction Scores from 140 million simulated lobby pairings, providing a robust foundation for fair matchmaking thresholds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
