Busca em Árvore de Monte Carlo (MCTS)
Monte Carlo Tree Search (MCTS) é um algoritmo poderoso usado em inteligência artificial for making decisions in games and other areas requiring strategic planning. MCTS combines the concepts of amostragem aleatória 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:
- Seleção: 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 (escolhendo jogadas que anteriormente levaram a resultados favoráveis).
- Expansão: Once a nó folha is reached (a node without children), one or more child nodes are added to represent new possible moves.
- Simulação: 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.
- Retropropagação: 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 tomada de decisão sob incerteza.