We present a state of the art implementation of the Monte Carlo Tree Search algorithm for
the game of Go. Our Pachi software is currently one of the strongest open source Go …

This paper proposes a novel molecular simulation method, called tree search molecular
dynamics (TS-MD), to accelerate the sampling of conformational transition pathways, which …

It is common practice to use large computational resources to train neural networks, as is
known from many examples, such as reinforcement learning applications. However, while …

Large-scale, parallel clusters composed of commodity processors are increasingly
available, enabling the use of vast processing capabilities and distributed RAM to solve hard …

Monte Carlo tree search (MCTS) is being effectively used in many domains, but acquiring
good results from building larger trees takes time that can in many cases be impractical. In …

This paper considers the capacity expansion problem in two-sided matchings, where the
policymaker is allowed to allocate some extra seats as well as the standard seats. In medical …

Abstract Monte-Carlo Tree Search (MCTS) is an adaptive and heuristic tree-search
algorithm designed to uncover sub-optimal actions at each decision-making point. This …

Background Artificially synthesized RNA molecules provide important ways for creating a
variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can …

In this paper we introduce a novel algorithm for online distributed non-myopic task-selection
in heterogeneous robotic teams. Our algorithm uses a temporal probabilistic representation …

Abstract Monte-Carlo Tree Search (MCTS) is a search paradigm that first found prominence
with its success in the domain of computer Go. Early theoretical work established the …