®®®® SIIA Público

Título del libro: 2020 Ieee Symposium Series On Computational Intelligence, Ssci 2020
Título del capítulo: Playing Carcassonne with Monte Carlo Tree Search

Autores UNAM:
ANGEL FERNANDO KURI MORALES;
Autores externos:

Idioma:

Año de publicación:
2020
Palabras clave:

Heuristic methods; Human computer interaction; Intelligent computing; Stochastic systems; Domain specific; Long-term strategy; Monte Carlo tree search (MCTS); Monte-Carlo tree searches; Multiple variants; Sampling method; Scoring systems; Value estimation; Monte Carlo methods


Resumen:

Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domainspecific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE. © 2020 IEEE.


Entidades citadas de la UNAM: