We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary.
They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Experiments in this paper demonstrate the performance of MESB in Hearthstone. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. We then discuss the implications of our findings and possible directions of future researches. The results show that the amount of enemy genes in initial genotypes influences the population’s fitness progression. We ran 300 generations in total and each was done in 100 iterations and had 40 individual genotypes. The genotype of an individual was represented by a 5x40 grid containing enemy genes and empty space genes. The fitness of difficulty curve of an enemyįormation was acquired through point-by-point comparison between the curve and a human-designed one, which represented the ideal difficulty curve as intended by the game’s developer, and calculation of the root-mean-square error. The difficulty curve took into account difficulty points, which measured the danger levels of onscreen enemies throughout the game’s duration. Generations of the formations were done with genetic algorithm where the fitness function calculated two variables: difficulty curve, which reflects a formation’s difficulty level, and enemy variety.
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In this paper we experimented with how to procedurally generate enemy formations in a vertical scrolling shooter game while keeping the difficulty levels proper. As proper difficulty levels are essential to player’s enjoyment, the enemy formations need to be arranged properly. In any game where the player must face large formations of enemies, the way the formations are formed significantly influences the difficulty level of the game.