Policies of Multiple Skill Levels for Better Strength Estimation in Games

TL;DR

This paper introduces an improved method for estimating human skill levels in games by incorporating behavioral policies alongside traditional strength scores, leading to more accurate and adaptive AI challenges.

Contribution

The study proposes a novel strength estimation approach that combines neural network-derived policies with strength scores, enhancing accuracy over previous methods.

Findings

Achieved 80% accuracy with 10 matches in Go, improving to 92% with 20 matches.

Outperformed previous state-of-the-art methods by 8-9% in accuracy.

Demonstrated similar improvements in chess, validating the method's effectiveness.

Abstract

Accurately estimating human skill levels is crucial for designing effective human-AI interactions so that AI can provide appropriate challenges or guidance. In games where AI players have beaten top human professionals, strength estimation plays a key role in adapting AI behavior to match human skill levels. In a previous state-of-the-art study, researchers have proposed a strength estimator trained using human players' match data. Given some matches, the strength estimator computes strength scores and uses them to estimate player ranks (skill levels). In this paper, we focus on the observation that human players' behavior tendency varies according to their strength and aim to improve the accuracy of strength estimation by taking this into account. Specifically, in addition to strength scores, we obtain policies for different skill levels from neural networks trained using human players' match data. We then combine features based on these policies with the strength scores to estimate strength. We conducted experiments on Go and chess. For Go, our method achieved an accuracy of 80% in strength estimation when given 10 matches, which increased to 92% when given 20 matches. In comparison, the previous state-of-the-art method had an accuracy of 71% with 10 matches and 84% with 20 matches, demonstrating improvements of 8-9%. We observed similar improvements in chess. These results contribute to developing a more accurate strength estimation method and to improving human-AI interaction.