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Results for "supervisors_tesim:"Mueller, Martin (Computing Science)""
The Monte-Carlo Tree Search (MCTS) algorithm Upper Confidence bounds applied to Trees (UCT) has become extremely popular in computer games research. Because of the importance of this family of algorithms, a deeper understanding of when and how their different enhancements work is desirable. To...
Monte Carlo tree search (MCTS) combined with the upper confidence bounds applied to trees (UCT) algorithm has brought forth many advances in game related AI. This includes general game players and programs for specific games such as Amazons, Arimaa, and Go. However, there often is a need for...
This thesis introduces random walk (RW) planning as a new search paradigm for satisficing planning by studying its theory, its practical relevance, and applications. We develop a theoretical framework that explains the strengths and weaknesses of random walks as a tool for heuristic search....
Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across...