  Quality diversity (QD) algorithms such as MAP-Elites have emerged as a
powerful alternative to traditional single-objective optimization methods. They
were initially applied to evolutionary robotics problems such as locomotion and
maze navigation, but have yet to see widespread application. 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.
  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. To avoid overpopulating cells with conflated behaviors, MESB slides the
boundaries of cells based on the distribution of evolved individuals.
Experiments in this paper demonstrate the performance of MESB in Hearthstone.
Results suggest MESB finds diverse ways of playing the game well along the
selected behavioral dimensions. Further analysis of the evolved strategies
reveals common patterns that recur across behavioral dimensions and explores
how MESB can help rebalance the game.
