提出在线更新的反制关系评分系统，实时优化玩家技能与策略对抗关系。
Online Learning of Counter Categories and Ratings in PvP Games
- 基于埃洛评分思想，动态更新玩家评分和反制类别。
- 在零和对战游戏中实现比传统方法更优的匹配效果。
- 适合需要实时调整策略对抗关系的竞技类游戏场景。
在竞技类游戏中，埃洛等标量评分广泛用于量化玩家实力，通过考虑实力差异来改进匹配机制，优于简单的胜率统计。然而，标量评分无法处理复杂的非传递性关系，如石头剪刀布中的策略克制。为此，近期研究引入了神经评分表与神经反制表，将标量评分与离散反制类别结合以建模非传递性。尽管有效，这些方法依赖神经网络训练，无法实时更新。本文提出一种在线更新算法，扩展埃洛原则，实现反制类别与评分的实时学习。该方法在每场对战后动态调整评分与反制关系，保持标量评分的可解释性同时解决非传递性问题。在零和竞技游戏上的实验表明其实用性，尤其在无复杂组队结构的场景中表现良好。
原文摘要 · Abstract (English)
In competitive games, strength ratings like Elo are widely used to quantify player skill and support matchmaking by accounting for skill disparities better than simple win rate statistics. However, scalar ratings cannot handle complex intransitive relationships, such as counter strategies seen in Rock-Paper-Scissors. To address this, recent work introduced Neural Rating Table and Neural Counter Table, which combine scalar ratings with discrete counter categories to model intransitivity. While effective, these methods rely on neural network training and cannot perform real-time updates. In this paper, we propose an online update algorithm that extends Elo principles to incorporate real-time learning of counter categories. Our method dynamically adjusts both ratings and counter relationships after each match, preserving the explainability of scalar ratings while addressing intransitivity. Experiments on zero-sum competitive games demonstrate its practicality, particularly in scenarios without complex team compositions.
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