2 research outputs found
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
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.Comment: Accepted to the Genetic and Evolutionary Computation Conference
(GECCO-2019
Evolving the Hearthstone Meta
Balancing an ever growing strategic game of high complexity, such as
Hearthstone is a complex task. The target of making strategies diverse and
customizable results in a delicate intricate system. Tuning over 2000 cards to
generate the desired outcome without disrupting the existing environment
becomes a laborious challenge. In this paper, we discuss the impacts that
changes to existing cards can have on strategy in Hearthstone. By analyzing the
win rate on match-ups across different decks, being played by different
strategies, we propose to compare their performance before and after changes
are made to improve or worsen different cards. Then, using an evolutionary
algorithm, we search for a combination of changes to the card attributes that
cause the decks to approach equal, 50% win rates. We then expand our
evolutionary algorithm to a multi-objective solution to search for this result,
while making the minimum amount of changes, and as a consequence disruption, to
the existing cards. Lastly, we propose and evaluate metrics to serve as
heuristics with which to decide which cards to target with balance changes.Comment: IEEE Conference on Games 2019. 8 page