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 …

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 …

Abstract Domain knowledge is crucial for effective performance in autonomous control
systems. Typically, human effort is required to encode this knowledge into a control …

Temporal-difference learning is one of the most successful and broadly applied solutions to
the reinforcement learning problem; it has been used to achieve master-level play in chess …

Skat is Germany's national card game played by millions of players around the world. In this
paper, we present the world's first computer skat player that plays at the level of human …

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

Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many
challenging benchmarks (eg, Computer Go). However, they generally require a large …

In this thesis, we investigate the problem of decision-making in large two-player zero-sum
games using Monte Carlo sampling and regret minimization methods. We demonstrate four …

Perfect information Monte Carlo (PIMC) search is the method of choice for constructing
strong Al systems for trick-taking card games. PIMC search evaluates moves in imperfect …