Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and
its applications have extended to the realm of video games. The incorporation of AI in video …

Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved wide-
spread adoption within the games community. Its links to traditional reinforcement learning …

$\textit {Magic: The Gathering} $ is a popular and famously complicated trading card game
about magical combat. In this paper we show that optimal play in real-world $\textit {Magic} …

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 …

Collectible card games have been among the most popular and profitable products of the
entertainment industry since the early days of Magic: The Gathering TM in the nineties …

One of the most notable features of collectible card games is deckbuilding, that is, defining a
personalized deck before the real game. Deckbuilding is a challenge that involves a big and …

In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game"
Hearthstone: Heroes of Warcraft". We argue that, in light of the challenges posed by the …

Digital collectible card games are not only a growing part of the video game industry, but
also an interesting research area for the field of computational intelligence. This game genre …

We apply the extension of Monte Carlo Tree Search for single player games (SP-MCTS) to
Sokoban and compare its performance to a solver integrating Iterative Deepening A*(IDA*) …