# Strength Estimation and Human-Like Strength Adjustment in Games

arXiv: https://arxiv.org/abs/2502.17109 · HTML: https://mlsanity.com/paper/2502.17109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [α-Rank: Multi-Agent Evaluation by Evolution](https://mlsanity.com/paper/1903.01373/) (2019)
- [Re-evaluating Evaluation](https://mlsanity.com/paper/1806.02643/) (2018)
- [ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero](https://mlsanity.com/paper/1902.04522/) (2019)
- [On the Limitations of Elo: Real-World Games, are Transitive, not Additive](https://mlsanity.com/paper/2206.12301/) (2022)
- [MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games](https://mlsanity.com/paper/2310.11305/) (2023)
- [AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner](https://mlsanity.com/paper/2111.06266/) (2021)
- [Dynamic Difficulty Adjustment via Fast User Adaptation](https://mlsanity.com/paper/2006.15545/) (2020)
- [Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection](https://mlsanity.com/paper/2009.01606/) (2020)
- [Ordinal Potential-based Player Rating](https://mlsanity.com/paper/2306.05366/) (2023)

## Built on by (most cited first, 1 of 1)

- [Policies of Multiple Skill Levels for Better Strength Estimation in Games](https://mlsanity.com/paper/2505.00279/) (2025)

## Abstract (arXiv)

Strength estimation and adjustment are crucial in designing human-AI interactions, particularly in games where AI surpasses human players. This paper introduces a novel strength system, including a strength estimator (SE) and an SE-based Monte Carlo tree search, denoted as SE-MCTS, which predicts strengths from games and offers different playing strengths with human styles. The strength estimator calculates strength scores and predicts ranks from games without direct human interaction. SE-MCTS utilizes the strength scores in a Monte Carlo tree search to adjust playing strength and style. We first conduct experiments in Go, a challenging board game with a wide range of ranks. Our strength estimator significantly achieves over 80% accuracy in predicting ranks by observing 15 games only, whereas the previous method reached 49% accuracy for 100 games. For strength adjustment, SE-MCTS successfully adjusts to designated ranks while achieving a 51.33% accuracy in aligning to human actions, outperforming a previous state-of-the-art, with only 42.56% accuracy. To demonstrate the generality of our strength system, we further apply SE and SE-MCTS to chess and obtain consistent results. These results show a promising approach to strength estimation and adjustment, enhancing human-AI interactions in games. Our code is available at https://rlg.iis.sinica.edu.tw/papers/strength-estimator.