8 research outputs found
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
We focus on the challenge of finding a diverse collection of quality
solutions on complex continuous domains. While quality diver-sity (QD)
algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are
designed to generate a diverse range of solutions, these algorithms require a
large number of evaluations for exploration of continuous spaces. Meanwhile,
variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are
among the best-performing derivative-free optimizers in single-objective
continuous domains. This paper proposes a new QD algorithm called Covariance
Matrix Adaptation MAP-Elites (CMA-ME). Our new algorithm combines the
self-adaptation techniques of CMA-ES with archiving and mapping techniques for
maintaining diversity in QD. Results from experiments based on standard
continuous optimization benchmarks show that CMA-ME finds better-quality
solutions than MAP-Elites; similarly, results on the strategic game Hearthstone
show that CMA-ME finds both a higher overall quality and broader diversity of
strategies than both CMA-ES and MAP-Elites. Overall, CMA-ME more than doubles
the performance of MAP-Elites using standard QD performance metrics. These
results suggest that QD algorithms augmented by operators from state-of-the-art
optimization algorithms can yield high-performing methods for simultaneously
exploring and optimizing continuous search spaces, with significant
applications to design, testing, and reinforcement learning among other
domains.Comment: Accepted to GECCO 202
Collaborative agent gameplay in the Pandemic board game
While artificial intelligence has been applied to control players’
decisions in board games for over half a century, little attention
is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to
overcome challenges posed by events occurring during the game’s
progression. This paper proposes an artificial agent which controls
all players’ actions and balances chances of winning versus risk
of losing in this highly stochastic environment. The agent applies
a Rolling Horizon Evolutionary Algorithm on an abstraction of
the game-state that lowers the branching factor and simulates the
game’s stochasticity. Results show that the proposed algorithm
can find winning strategies more consistently in different games
of varying difficulty. The impact of a number of state evaluation
metrics is explored, balancing between optimistic strategies that
favor winning and pessimistic strategies that guard against losing.peer-reviewe
Analysis of gameplay strategies in hearthstone: a data science approach
In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill level than those implemented in one such simulator, SabberStone. New benchmarks are propsed using supervised learning techniques to predict gameplay strategies from game data, and using unsupervised learning techniques to discover and visualize patterns that may be used in player modeling to differentiate gameplay strategies