alpha–beta pruning
E748469
Alpha–beta pruning is a search algorithm optimization that reduces the number of nodes evaluated in minimax-based game tree searches by eliminating branches that cannot affect the final decision.
All labels observed (1)
| Label | Occurrences |
|---|---|
| alpha–beta pruning canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8672501 — 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: alpha–beta pruning Context triple: [Monte Carlo tree search, relatedTo, alpha–beta pruning]
-
A.
Monte Carlo tree search
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.
-
B.
Davis–Putnam algorithm
The Davis–Putnam algorithm is a pioneering procedure in automated theorem proving and propositional logic satisfiability that laid foundational groundwork for modern SAT solvers.
-
C.
Generalized Search Tree
Generalized Search Tree is a flexible, balanced tree data structure framework that supports building custom index types for complex data and queries, often used in database systems.
-
D.
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.
-
E.
Benettin algorithm
The Benettin algorithm is a numerical method used in dynamical systems theory to estimate Lyapunov exponents, which quantify the rate of separation of nearby trajectories and indicate chaos.
- 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: alpha–beta pruning Target entity description: Alpha–beta pruning is a search algorithm optimization that reduces the number of nodes evaluated in minimax-based game tree searches by eliminating branches that cannot affect the final decision.
-
A.
Monte Carlo tree search
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.
-
B.
Davis–Putnam algorithm
The Davis–Putnam algorithm is a pioneering procedure in automated theorem proving and propositional logic satisfiability that laid foundational groundwork for modern SAT solvers.
-
C.
Generalized Search Tree
Generalized Search Tree is a flexible, balanced tree data structure framework that supports building custom index types for complex data and queries, often used in database systems.
-
D.
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.
-
E.
Benettin algorithm
The Benettin algorithm is a numerical method used in dynamical systems theory to estimate Lyapunov exponents, which quantify the rate of separation of nearby trajectories and indicate chaos.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
adversarial search algorithm
ⓘ
game tree search technique ⓘ search algorithm optimization ⓘ |
| alphaRepresents | best value that maximizer can guarantee so far ⓘ |
| alsoKnownAs | alpha-beta search NERFINISHED ⓘ |
| assumes |
deterministic game transitions
ⓘ
no hidden information ⓘ optimal play by both players ⓘ |
| basedOn | minimax algorithm ⓘ |
| belongsTo |
algorithm design
ⓘ
artificial intelligence ⓘ |
| benefitsFrom | good move ordering ⓘ |
| bestCaseComplexity | O(b^(d/2)) ⓘ |
| betaRepresents | best value that minimizer can guarantee so far ⓘ |
| canBeCombinedWith |
heuristic evaluation functions
ⓘ
iterative deepening search ⓘ transposition tables ⓘ |
| category | depth-first search enhancement ⓘ |
| commonlyAppliedIn |
computer Go variants with minimax search
ⓘ
computer Othello ⓘ computer checkers ⓘ computer chess ⓘ |
| doesNotChange | final minimax value with perfect play ⓘ |
| eliminates | branches that cannot affect final decision ⓘ |
| enables | deeper search within same time limit ⓘ |
| ensures | same decision as full minimax search if implemented correctly ⓘ |
| formalizedBy | John McCarthy NERFINISHED ⓘ |
| goal | reduce number of nodes evaluated in game tree search ⓘ |
| improves | search efficiency ⓘ |
| influenced | design of modern game-playing engines ⓘ |
| introducedInContextOf | game-playing programs ⓘ |
| mathematicallyRelatedTo | branch and bound ⓘ |
| optimizes | minimax-based game tree search ⓘ |
| prunes | subtrees that cannot improve current best outcome ⓘ |
| prunesWhen | alpha is greater than or equal to beta ⓘ |
| reduces | time complexity constant factor ⓘ |
| requires | two bounds alpha and beta ⓘ |
| typicallyUsedWith | depth-first minimax search ⓘ |
| typicalUseCase | searching game trees to a fixed depth with evaluation at leaves ⓘ |
| usedIn |
perfect-information games
ⓘ
turn-based games ⓘ two-player zero-sum games ⓘ |
| where |
b is branching factor
ⓘ
d is search depth ⓘ |
| worstCaseComplexity | O(b^d) ⓘ |
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: alpha–beta pruning Description of subject: Alpha–beta pruning is a search algorithm optimization that reduces the number of nodes evaluated in minimax-based game tree searches by eliminating branches that cannot affect the final decision.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.