Although artificial intelligence systems can now outperform humans in a variety of domains,
they still lag behind in the ability to arrive at good solutions to problems using limited …

The success of Monte Carlo tree search (MCTS) in many games, where αβ-based search
has failed, naturally raises the question whether Monte Carlo simulations will eventually also …

Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such
as Go and Atari games when combining with deep neural networks (DNNs). When more …

The thesis deals with the question how opponent models can be used in computer game-
playing. The search algorithms normally used in programs that play games like chess are …

A new approach for heuristic game-tree search, probabilistic opponent-model search (PrOM
search), is proposed. It is based on standard opponent-model search (OM search). The new …

Monte Carlo Tree Search (MCTS) is a popular approach for tree search in a variety of
games. While MCTS allows for fine-grained time control, not much has been published on …

Chess includes two significant factors: playing good moves and managing your time
optimally. Time, especially in blitz games, is just as essential to the game as making good …

This thesis investigates how search can be guided by knowledge in such a way that the
search space is traversed efficiently and effectively. For this task we focus on the question …

This thesis is concerned with enhancing the technique of Monte-Carlo Tree Search (MCTS),
applied to making move decisions in games. MCTS has become the dominating paradigm in …

Opponent-model (OM) search comes with two types of risk. The first type is caused by a
player's imperfect knowledge of the opponent, the second type arises from low-quality …