Online Learning of Counter Categories and Ratings in PvP Games
Chiu-Chou Lin, I-Chen Wu

TL;DR
This paper introduces an online algorithm that updates player ratings and counter relationships in real-time for PvP games, addressing the limitations of scalar ratings in modeling intransitive strategies like rock-paper-scissors.
Contribution
It extends Elo-based ratings with an online update mechanism for counter categories, enabling real-time learning of complex intransitive relationships.
Findings
Effective in zero-sum competitive games
Handles intransitivity without neural networks
Suitable for real-time matchmaking scenarios
Abstract
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.…
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Taxonomy
TopicsGame Theory and Applications · Artificial Intelligence in Games · Auction Theory and Applications
