There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …

Games have a long history as benchmarks for progress in artificial intelligence. Approaches
using search and learning produced strong performance across many perfect information …

Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to
many deterministic games of perfect information. This paper investigates the application of …

Artificial Intelligence has seen several breakthroughs in two-player perfect information
game. Nevertheless, Doudizhu, a three-player imperfect information game, is still quite …

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 …

In this paper, we examine the use of Monte Carlo tree search (MCTS) for a variant of one of
the most popular and profitable games in the world: the card game Magic: The Gathering (M …

Online search in games has been a core interest of artificial intelligence. Search in imperfect
information games (eg, Poker, Bridge, Skat) is particularly challenging due to the …

Determinization is a technique for making decisions in games with stochasticity and/or
imperfect information by sampling instances of the equivalent deterministic game of perfect …

GDL-III, a description language for general game playing with imperfect information and
introspection, supports the specification of epistemic games. These are characterised by …