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 …

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 …

Fuego is both an open-source software framework and a state-of-the-art program that plays
the game of Go. The framework supports developing game engines for full-information two …

The aim of general game playing (GGP) is to create intelligent agents that can automatically
learn how to play many different games at an expert level without any human intervention …

Imitation learning aims to extract high-performance policies from logged demonstrations of
expert behavior. It is common to frame imitation learning as a supervised learning problem …

The discovery of patterns that accurately discriminate one class label from another remains
a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that …

With the recent success of Monte-Carlo tree search algorithms in Go and other games, and
the increasing number of cores in standard CPUs, the efficient parallelization of the search …

The parallelization of MCTS across multiple-machines has proven surprisingly difficult. The
limitations of existing algorithms were evident in the 2009 Computer Olympiad where Zen …

We have parallelized our general game player Ary on a cluster of computers. We propose
multiple parallelization algorithms. For the sake of simplicity all our algorithms have …

Parallelizing Monte Carlo tree search (MCTS) has been considered to be a way to improve
the strength of Computer Go programs. In this paper, we analyze the performance of two root …