Quoridor agent using Monte Carlo Tree Search

This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation

Quoridor agent using Monte Carlo Tree Search

This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation

Quoridor agent using Monte Carlo Tree Search

This thesis presents a preliminary study using Monte Carlo Tree Search (MCTS) upon the board game of Quoridor. The system is shown to perform well against current existing methods, defeating a set of player agents drawn from an existing digital implementation

Development of Multiple Behaviors in Evolving Robots

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered