Hendrik Baier

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The dominant paradigm for programs playing the game of Go is Monte Carlo tree search. This algorithm builds a search tree by playing many simulated games (playouts). Each playout consists of a sequence of moves within the tree followed by many moves beyond the tree. Moves beyond the tree are generated by a biased random sampling policy. The recently(More)
Monte-Carlo Tree Search is a sampling-based search algorithm that has been successfully applied to a variety of games. Monte-Carlo rollouts allow it to take distant consequences of moves into account, giving it a strategic advantage in many domains over traditional depth-limited minimax search with alpha-beta pruning. However, MCTS builds a highly selective(More)
Monte-Carlo Tree Search (MCTS) is state of the art for online planning in large MDPs. It is a best-first, sample-based search algorithm in which every state in the search tree is evaluated by the average outcome of Monte-Carlo rollouts from that state. These rollouts are typically random or directed by a simple, domain-dependent heuristic. We propose Nested(More)
Interaction between human rescue workers and rescue systems provide a great challenge. The proposed system involves mobile robots that explore the emergency area, in order to reduce the risk for human life and a localization system for the human rescuers. Human and robotic team members are supported, guided and coordinated by a supervisor. Remote sensor(More)
Monte-Carlo Tree Search (MCTS) is a state-of-the-art stochastic search algorithm that has successfully been applied to various multi- and one-player games (puzzles). Beam search is a search method that only expands a limited number of promising nodes per tree level, thus restricting the space complexity of the underlying search algorithm to linear in the(More)
The dominant approach for programs playing the Asian board game of Go is nowadays Monte-Carlo Tree Search (MCTS). However, MCTS does not perform well in the opening phase of the game, as the branching factor is high and consequences of moves can be far delayed. Human knowledge about Go openings is typically captured in joseki, local sequences of moves that(More)
Monte Carlo tree search (MCTS) is a sampling-based search algorithm that is state of the art in a variety of games. In many domains, its Monte Carlo rollouts of entire games give it a strategic advantage over traditional depth-limited minimax search with αβ pruning. These rollouts can often detect long-term consequences of moves, freeing the(More)
ACFA 2020 (active control for flexible aircraft) is a collaborative research project funded by the European Commission under the seventh research framework programme. The project deals with innovative active control concepts for 2020 aircraft configurations like the blended wing body (BWB) aircraft. The main objectives of ACFA are the design of a new ultra(More)