Monte Carlo tree search
E205830
Monte Carlo method
decision-making technique
game tree search method
heuristic algorithm
search algorithm
Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Monte Carlo tree search canonical | 4 |
| Monte Carlo Tree Search | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T1844415 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo tree search Context triple: [David Silver, researchInterest, Monte Carlo tree search]
-
A.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
B.
AlphaZero
AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
-
C.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
-
D.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
E.
AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo tree search Target entity description: Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
-
A.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
B.
AlphaZero
AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
-
C.
AlphaGo
AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
-
D.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
E.
AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Monte Carlo method
ⓘ
decision-making technique ⓘ game tree search method ⓘ heuristic algorithm ⓘ search algorithm ⓘ |
| abbreviation | MCTS ⓘ |
| advantage |
requires minimal domain knowledge
ⓘ
scales well with additional computation time ⓘ |
| appliedIn |
artificial intelligence research
ⓘ
board games ⓘ combinatorial optimization ⓘ computer Go ⓘ general game playing ⓘ planning problems ⓘ puzzles ⓘ real-time video games ⓘ robotics ⓘ |
| canUse |
domain-specific heuristics
ⓘ
neural network policy priors ⓘ neural network value functions ⓘ |
| developedInContextOf | computer Go research ⓘ |
| goal | select strong actions in complex decision spaces ⓘ |
| hasComponent |
backpropagation phase
ⓘ
expansion phase ⓘ selection phase ⓘ simulation phase ⓘ |
| input | current game state ⓘ |
| notableUse | strong Go programs before deep learning era ⓘ |
| oftenUses | upper confidence bounds applied to trees ⓘ |
| optimizes | exploration–exploitation trade-off ⓘ |
| output |
estimated value of positions
ⓘ
recommended move ⓘ |
| property |
anytime algorithm
ⓘ
asymmetric tree growth ⓘ can be interrupted at any time with best-so-far move ⓘ can handle large branching factors ⓘ does not require full game tree enumeration ⓘ |
| relatedTo |
alpha–beta pruning
ⓘ
minimax search ⓘ multi-armed bandit problem ⓘ upper confidence bound algorithm ⓘ |
| requires |
rollout strategy
ⓘ
simulation policy ⓘ |
| usedIn |
AlphaGo search procedure
ⓘ
AlphaZero search procedure ⓘ MuZero search procedure ⓘ |
| uses |
Monte Carlo simulations
ⓘ
playout simulations ⓘ random sampling of game states ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Monte Carlo tree search Description of subject: Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Monte Carlo Tree Search