Demonstrating the Feasibility of Automatic Game Balancing

TL;DR

This paper demonstrates that automatic game balancing using multi-objective evolutionary algorithms and simulation-based objectives is feasible, producing balanced decks comparable to published ones, even for complex games.

Contribution

It introduces a multi-objective approach for automatic game balancing and validates its effectiveness on the card game Top Trumps.

Findings

Automatic balancing can produce decks as good as published ones.

Multi-objective optimization improves balancing quality.

Feasibility extends to complex games like real-time strategy.

Abstract

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.