Recherche par arbre de Monte Carlo (MCTS)
La recherche par arbre Monte Carlo (MCTS) est un algorithme puissant utilisé dans intelligence artificielle for making decisions in games and other areas requiring strategic planning. MCTS combines the concepts of échantillonnage aléatoire and a tree structure to evaluate potential moves and outcomes in a decision-making process.
The algorithm works by incrementally building a search tree, where each node represents a game state and each edge represents a potential move. The process consists of four main steps:
- Sélection : Starting from the root node, the algorithm traverses the tree by selecting child nodes based on a balance between exploration (trying new moves) and exploitation (le choix de coups qui ont précédemment conduit à des résultats favorables).
- Expansion : Once a nœud feuille is reached (a node without children), one or more child nodes are added to represent new possible moves.
- Simulation: From the newly added node, a simulation or rollout is performed, where random moves are played until the game reaches a terminal state (win, lose, or draw). This provides an estimate of the outcome.
- Rétropropagation: The result of the simulation is propagated back up the tree, updating the statistics of all nodes traversed. This helps refine the decision-making process based on the outcomes of the simulations.
MCTS is particularly effective in domains with large search spaces and uncertain outcomes, such as board games like Go and Chess, but it has also found applications in various fields including robotics, optimization, and artificial intelligence in general. Its ability to balance exploration and exploitation makes it a versatile tool for prise de décision en situation d’incertitude.