Monte Carlo Tree Search

Type

Heuristic search algorithm

Field

Artificial Intelligence, Computer Science

Purpose

Decision-making processes, game playing, solving sequential decision problems

Mechanism

Intelligent tree search, balances exploration and exploitation, uses random sampling/simulations, incrementally builds a search tree

Advantages

Effective for problems with vast search spaces, extends AI capabilities, performs reasonably well on difficult problems

Core Steps

Selection, Expansion, Simulation (Playout/Rollout), Backup

Limitations

Computationally intensive, may struggle in domains with ill-defined state transitions or extremely vast search spaces, requires problem-specific modifications for complex/real-time applications

1980s (late)

2006

2008

Monte Carlo Tree Search (MCTS) is a heuristic algorithm used in AI decision-making processes, particularly in complex situations. It employs intelligent tree search techniques to assist AI in making informed choices. MCTS is widely applied in various domains such as game simulation, robotics, and security. In MCTS, nodes symbolize states or problem configurations, while edges signify transitions between these states. The algorithm's fundamental steps include selection, expansion, simulation, and backup, which collectively contribute to decision-making processes by evaluating potential outcomes and selecting the most favorable path.

Monte Carlo tree search (MCTS) is a heuristic search algorithm for decision processes. Here’s what you need to know.

Abstract page for arXiv paper 2103.04931: Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Explore Game AI: Dive into AlphaGo, tree search algorithms, and Monte Carlo Tree Search (MCTS) for a deeper understanding of AI in gaming.

The Animated Monte-Carlo Tree Search (MCTS) The algorithm at the heart of AlphaGo, AlphaGo Zero, AlphaZero and MuZero Monte-Carlo Tree Search (MCTS) is a heuristic search method, whose main purpose …

In computer science, Monte Carlo tree search (MCTS) is a heuristic "Heuristic (computer science)") search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree. [...] The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space. The application of Monte Carlo tree search in games is based on many playouts, also called roll-outs. In each playout, the game is played out to the very end by selecting moves at random. The final game result of each playout is then used to weight the nodes in the game tree so that better nodes are more likely to be chosen in future playouts. [...] In 2006, inspired by its predecessors, Rémi Coulom described the application of the Monte Carlo method to game-tree search and coined the name Monte Carlo tree search, L. Kocsis and Cs. Szepesvári developed the UCT (Upper Confidence bounds applied to Trees) algorithm, and S. Gelly et al. implemented UCT in their program MoGo. In 2008, MoGo achieved dan "Dan (rank)") (master) level in 9×9 Go, and the Fuego program began to win against strong amateur players in 9×9 Go.

Monte Carlo Tree Search (MCTS) is a algorithm designed for problems with extremely large decision spaces, like the game Go with its 10170possible board states. Instead of exploring all moves, MCTS incrementally builds a search tree using random simulations (rollouts) to guide its decisions. It balances exploration of new possibilities and usage of known promising paths, effectively focusing computational effort where it matters most, making it highly efficient for complex decision-making tasks.

> Abstract:Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. those with

Monte Carlo Tree Search (MCTS) is a relatively new Artificial Intelligence (AI) algorithm that has gained significant attention and success in the field of games. The algorithm has been described as a general-purpose heuristic, a search process that can be applied to a wide range of different problems and applications and remain quite effective within all of them. This paper will look at a recent increase in interest in this algorithm from within industrial research. It will look at the topics [...] 2. Literature Review Monte Carlo algorithms, or algorithms with a stochastic component, are at least as old as modern computers. Monte Carlo Tree Search is a relatively recent addition to this collection. It has its roots in research conducted in the late 1980s into stochastic tree search methods for two-player games and was received its current name 2006 . [...] is a relatively recent Artificial Intelligence algorithm, sometimes described as a general-purpose heuristic, that has been found to be very effective in several theoretical and game-related problems. This paper will review the current growth in research into possible industrial applications of this algorithm and how a framework utilising this algorithm can help to realise the aims of the smart factory vision.