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 …

A new paradigm for search, based on Monte-Carlo simulation, has revolutionised the
performance of computer Go programs. In this article we describe two extensions to the …

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

The use of robots in society could be expanded by using reinforcement learning (RL) to
allow robots to learn and adapt to new situations online. RL is a paradigm for learning …

This thesis studies the use of Monte-Carlo simulations for tree-search problems. The Monte-
Carlo technique we investigate is Monte-Carlo Tree Search (MCTS). It is a best-first search …

In order to promote computer Go and stimulate further development and research in the
field, the event activities, Computational Intelligence Forum and World 9 \,*\, 9 Computer Go …

The multi-armed bandit problem has attracted remarkable attention in the machine learning
community and many efficient algorithms have been proposed to handle the so-called …

This paper analyses extensions of No-Free-Lunch (NFL) theorems to countably infinite and
uncountable infinite domains and investigates the design of optimal optimization algorithms …

Learning and planning are two fundamental problems in artificial intelligence. The learning
problem can be tackled by reinforcement learning methods, such as temporal-difference …

This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for
the game of Go, shows the many similarities and the rare differences between the current …