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 artificial players for the prediction of gameplay and outcome. In this paper, the feasibility of automatic balancing is investigated for the card game top trumps using simulation-and deck-based objectives. Additionally, the necessity of a multi-objective approach is asserted by assessing the only published (singleobjective) method. We apply a multi-objective evolutionary algorithm to obtain decks that optimise objectives developed to express the fairness and the excitement of a game of top trumps, e.g. win rate and average number of tricks. The results are compared with decks from published top trumps games using the aforementioned 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.
Volz et al. REF evaluate how close the game ended as well in the card game Top Trumps with the objective of automatically balancing the game.
Demonstrating the Feasibility of Automatic Game Balancing
GECCO '16
Computer Science