25 research outputs found
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms
We investigate the impact of supervised prediction models on the strength and
efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS)
algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We
overview our custom implementation of the MCTS that is well-suited for games
with partially hidden information and random effects. We also describe
experiments which we designed to quantify the performance of our Hearthstone
agent's decision making. We show that even simple neural networks can be
trained and successfully used for the evaluation of game states. Moreover, we
demonstrate that by providing a guidance to the game state search heuristic, it
is possible to substantially improve the win rate, and at the same time reduce
the required computations.Comment: Proceedings of the 2018 IEEE Conference on Computational Intelligence
and Games (CIG'18); pages 445-452; ISBN: 978-1-5386-4358-
Counterfactual Regret Minimization を用いたトレーディングカードゲームの戦略計算
学位の種別: 修士University of Tokyo(東京大学
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
In the last decades we have witnessed the success of applications of
Artificial Intelligence to playing games. In this work we address the
challenging field of games with hidden information and card games in
particular. Jass is a very popular card game in Switzerland and is closely
connected with Swiss culture. To the best of our knowledge, performances of
Artificial Intelligence agents in the game of Jass do not outperform top
players yet. Our contribution to the community is two-fold. First, we provide
an overview of the current state-of-the-art of Artificial Intelligence methods
for card games in general. Second, we discuss their application to the use-case
of the Swiss card game Jass. This paper aims to be an entry point for both
seasoned researchers and new practitioners who want to join in the Jass
challenge
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
Summarizing Strategy Card Game AI Competition
This paper concludes five years of AI competitions based on Legends of Code
and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal
of supporting research and algorithm development. The game was used in a number
of events, including Community Contests on the CodinGame platform, and Strategy
Card Game AI Competition at the IEEE Congress on Evolutionary Computation and
IEEE Conference on Games. LOCM has been used in a number of publications
related to areas such as game tree search algorithms, neural networks,
evaluation functions, and CCG deckbuilding. We present the rules of the game,
the history of organized competitions, and a listing of the participant and
their approaches, as well as some general advice on organizing AI competitions
for the research community. Although the COG 2022 edition was announced to be
the last one, the game remains available and can be played using an online
leaderboard arena
Mastering Strategy Card Game (Hearthstone) with Improved Techniques
Strategy card game is a well-known genre that is demanding on the intelligent
game-play and can be an ideal test-bench for AI. Previous work combines an
end-to-end policy function and an optimistic smooth fictitious play, which
shows promising performances on the strategy card game Legend of Code and
Magic. In this work, we apply such algorithms to Hearthstone, a famous
commercial game that is more complicated in game rules and mechanisms. We
further propose several improved techniques and consequently achieve
significant progress. For a machine-vs-human test we invite a Hearthstone
streamer whose best rank was top 10 of the official league in China region that
is estimated to be of millions of players. Our models defeat the human player
in all Best-of-5 tournaments of full games (including both deck building and
battle), showing a strong capability of decision making.Comment: cog2023 ful
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