Definition · foundations
Monte Carlo tree search
Monte Carlo tree search is a plan search that samples possible outcomes and spends more effort on promising paths. You can spot Monte Carlo tree search in practice when the record shows what the system observes, what choice it makes, and what changes next, without asking the reader to guess or trust a slogan.
157 definitions656 sourced recordsNamed source: ACL Anthology
Why does Monte Carlo tree search matter?
Sampling concentrates effort where simulated outcomes look promising, which can help when exhaustive search is too costly. For Monte Carlo tree search, start with three checks: What can the system observe? What choice can it make? What changes after it acts? The answers should point to visible evidence, not a promise.
What does Monte Carlo tree search look like?
A game agent simulates candidate moves and explores more deeply from moves that win more often. This example makes Monte Carlo tree search visible by naming the actor, the action, and the result in practice. It is an illustration for readers, not a claim that every product behaves this way.
What is Monte Carlo tree search easy to confuse with?
Monte Carlo tree search uses sampled outcomes. Heuristic search uses an estimate to guide which options are visited. The closest entries here are Moltiverse, natural-language interaction environment, planning via search. Compare their definitions with Monte Carlo tree search before using the names as if they mean the same thing.
How certain is this definition?
This definition is marked source checked because ACL Anthology supports its main points. The date shows when we last read that source. If you are making a purchase, policy, safety, or design decision, open the source first and make sure its limits fit the decision in front of you.
Who introduced the term Monte Carlo tree search?
The source shows how Monte Carlo tree search is used, but it does not prove who said it first. We name an origin only when a dated, direct record supports it. If that record is missing, we leave the question open instead of repeating a popular story as fact.
What should you understand before Monte Carlo tree search?
Read artificial intelligence (AI) first when Monte Carlo tree search depends on a more basic idea or mechanism. The link gives you a useful route through the glossary. It does not mean every author teaches the subject in this order, so follow the source when the sequence matters.
What should this term help you answer?
Use Monte Carlo tree search to ask what an agent can observe, decide, change, remember, or hand off. The questions below turn the definition into a practical check. They are prompts for your own work, not claims made by the source, so change them to fit the system you are examining.
- What can the system observe?
- What choice can it make?
- What changes after it acts?
Which source supports this definition of Monte Carlo tree search?
ACL Anthology supports this working definition of Monte Carlo tree search. We checked the link on 2026-07-12. Open the original record for its context, methods, limits, and publication details. The short explanation here is a guide to Monte Carlo tree search; the linked source remains the evidence to inspect when the wording or boundary matters.
direct supportMonte Carlo tree searchChecked 2026-07-12→