モンテカルロ木探索（MCTS）
Monte Carlo Tree Search (MCTS) is a powerful algorithm used in 人工知能 for making decisions in games and other areas requiring strategic planning. MCTS combines the concepts of ランダムサンプリング 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:
- 選択： 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 (choosing moves that have previously led to favorable outcomes).
- 展開： Once a 葉ノード is reached (a node without children), one or more child nodes are added to represent new possible moves.
- シミュレーション: 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.
- 逆伝播法: 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 不確実性の下での意思決定.