A hybrid methodology of game theory and Monte Carlo Tree Search was developed and the hybrid methodology was tested with various case studies through the nurse scheduling problem to show that it was able to form Pareto front dominance solutions, finding feasible solutions that were optimal and finding feasible partial solutions in over-constrained problems. The performance comparison was carried out with the Genetic Algorithm on the Resident Physician Scheduling problem and showed that the hybrid methodology was able to produce better quality solutions compared to the state of the art approach
Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in general game ...
International audienceIn this paper, we apply the Monte Carlo Tree Search (MCTS) method for controll...
Abstract Monte-Carlo tree search is a recent and powerful algorithm that has been applied with succe...
To optimize a combinatorial problem one can use complex algorithms, e.g. branchand- bound algorithms...
Abstract. Monte Carlo Tree Search is a recent algorithm that achieves more and more successes in var...
This paper highlights an experiment to see how standard Monte Carlo Tree Search handles simple co-op...
This study compares a player using Monte Carlo Tree Search (MCTS) against a variety of well-known Pr...
Multi-Objective optimization has traditionally been applied to manufacturing, engineering or finance...
Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In thi...
Abstract. Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantial...
The aim of this work is design, implementation and experimental evaluation of distributed algorithms...
International audienceMany state-of-the-art methods for combinatorial games rely on Monte Carlo Tree...
Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large s...
Abstract: Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial inte...
Classic approaches to game AI require either a high quality of domain knowledge, or a long time to g...
Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in general game ...
International audienceIn this paper, we apply the Monte Carlo Tree Search (MCTS) method for controll...
Abstract Monte-Carlo tree search is a recent and powerful algorithm that has been applied with succe...
To optimize a combinatorial problem one can use complex algorithms, e.g. branchand- bound algorithms...
Abstract. Monte Carlo Tree Search is a recent algorithm that achieves more and more successes in var...
This paper highlights an experiment to see how standard Monte Carlo Tree Search handles simple co-op...
This study compares a player using Monte Carlo Tree Search (MCTS) against a variety of well-known Pr...
Multi-Objective optimization has traditionally been applied to manufacturing, engineering or finance...
Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In thi...
Abstract. Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantial...
The aim of this work is design, implementation and experimental evaluation of distributed algorithms...
International audienceMany state-of-the-art methods for combinatorial games rely on Monte Carlo Tree...
Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large s...
Abstract: Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial inte...
Classic approaches to game AI require either a high quality of domain knowledge, or a long time to g...
Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in general game ...
International audienceIn this paper, we apply the Monte Carlo Tree Search (MCTS) method for controll...
Abstract Monte-Carlo tree search is a recent and powerful algorithm that has been applied with succe...