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 …

The ancient oriental game of Go has long been considered a grand challenge for artificial
intelligence. For decades, computer Go has defied the classical methods in game tree …

America's critical infrastructure is becoming “smarter” and increasingly dependent on highly
specialized computers called industrial control systems (ICS). Networked ICS components …

Abstract Upper Confidence Trees are a very efficient tool for solving Markov Decision
Processes; originating in difficult games like the game of Go, it is in particular surprisingly …

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 …

Monte Carlo tree search (MCTS) methods have had recent success in games, planning, and
optimization. MCTS uses results from rollouts to guide search; a rollout is a path that …

Concerned with multi-objective reinforcement learning (MORL), this paper presents MO-
MCTS, an extension of Monte-Carlo Tree Search to multi-objective sequential decision …

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

The commercial success of Cloud computing and recent developments in Grid computing
have brought platform virtualization technology into the field of high performance computing …

We define a probe to be a single action sequence computedgreedily from a given state that
either terminates in the goalor fails. We show that by designing these probes carefullyusing …