# Demonstrating the Feasibility of Automatic Game Balancing

arXiv: https://arxiv.org/abs/1603.03795 · HTML: https://mlsanity.com/paper/1603.03795/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2016.

## Built on by (most cited first, 5 of 5)

- [SuSketch: Surrogate Models of Gameplay as a Design Assistant](https://mlsanity.com/paper/2103.11726/) (2021)
- [Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation](https://mlsanity.com/paper/2107.02457/) (2021)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2503.18748/) (2025)
- [Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning](https://mlsanity.com/paper/2503.24099/) (2025)

## Abstract (arXiv)

Game balancing is an important part of the (computer) game design process, in which designers adapt a game prototype so that the resulting gameplay is as entertaining as possible. In industry, the evaluation of a game is often based on costly playtests with human players. It suggests itself to automate this process using surrogate models for the prediction of gameplay and outcome. In this paper, the feasibility of automatic balancing using simulation- and deck-based objectives is investigated for the card game top trumps. Additionally, the necessity of a multi-objective approach is asserted by a comparison with the only known (single-objective) method. We apply a multi-objective evolutionary algorithm to obtain decks that optimise objectives, e.g. win rate and average number of tricks, developed to express the fairness and the excitement of a game of top trumps. The results are compared with decks from published top trumps decks using simulation-based objectives. The possibility to generate decks better or at least as good as decks from published top trumps decks in terms of these objectives is demonstrated. Our results indicate that automatic balancing with the presented approach is feasible even for more complex games such as real-time strategy games.