Demonstrating the Feasibility of Automatic Game Balancing
This addresses the problem of costly manual playtesting in game design, offering an incremental improvement by extending automation to more complex games.
The paper tackled automating game balancing for the card game top trumps by using a multi-objective evolutionary algorithm to optimize decks for fairness and excitement, demonstrating that generated decks can be as good or better than published decks in terms of objectives like win rate and average number of tricks.
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.