AlphaGo Zero

E229130

Go-playing computer program artificial intelligence system computer program

AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (3)

Label	Occurrences
AlphaGo Zero canonical	9
Mastering the game of Go without human knowledge	2
Mastering the game of Go with deep neural networks and tree search	1

How this entity was disambiguated

This entity first appeared as the object of triple T1844402 — 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: AlphaGo Zero
Context triple: [David Silver, notableWork, AlphaGo Zero]

A. 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.
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. 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.
D. AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
E. DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
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: AlphaGo Zero
Target entity description: AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.

A. 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.
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. 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.
D. AlphaStar
AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
E. DeepMind
DeepMind is a leading artificial intelligence research company renowned for breakthroughs such as AlphaGo and deep reinforcement learning, operating as a subsidiary of Google.
F. None of above. chosen

Statements (52)

Predicate	Object
instanceOf	Go-playing computer program ⓘ artificial intelligence system ⓘ computer program ⓘ
achievedLevel	superhuman performance in Go ⓘ
architecture	single neural network for policy and value ⓘ
basedOn	Monte Carlo tree search ⓘ convolutional neural network ⓘ deep learning ⓘ reinforcement learning ⓘ
countryOfOrigin	United Kingdom ⓘ
describedInPublication	Mastering the game of Go without human knowledge ⓘ linked to: AlphaGo Zero
developer	DeepMind ⓘ DeepMind Technologies ⓘ linked to: DeepMind
differsFrom	AlphaGo ⓘ AlphaGo Lee ⓘ linked to: AlphaGo AlphaGo Master ⓘ linked to: AlphaGo
doesNotUse	human expert games ⓘ separate policy network ⓘ separate value network ⓘ
fieldOfApplication	computer Go ⓘ
fieldOfStudy	artificial intelligence ⓘ machine learning ⓘ reinforcement learning ⓘ
hasAuthor	Adrià Puigdomènech ⓘ linked to: Adrià Puigdomènech Badia Aja Huang ⓘ Arthur Guez ⓘ David Silver ⓘ Demis Hassabis ⓘ Dominik Grewe ⓘ Ioannis Antonoglou ⓘ John Nham ⓘ Julian Schrittwieser ⓘ Karen Simonyan ⓘ Koray Kavukcuoglu ⓘ Lucas Baker ⓘ Marc Lanctot ⓘ Matthew Lai ⓘ Pushmeet Kohli ⓘ Sander Dieleman ⓘ Thomas Hubert ⓘ Thore Graepel ⓘ Timothy Lillicrap ⓘ
inspiredDevelopmentOf	AlphaZero ⓘ
notableAchievement	surpassed performance of previous AlphaGo versions ⓘ
playsGame	Go ⓘ
publicationDate	2017-10-18 ⓘ
publicationVenue	Nature ⓘ
trainingObjective	maximize probability of winning Go games ⓘ
trainingStartState	random play ⓘ
usesTrainingData	no human game data ⓘ
usesTrainingMethod	self-play ⓘ tabula rasa learning ⓘ

How these facts were elicited

Referenced by (12)

Full triples — surface form annotated when it differs from this entity's canonical label.

David Silver → notableWork → AlphaGo Zero ⓘ

AlphaZero → outperforms → AlphaGo Zero ⓘ

MuZero → inspiredBy → AlphaGo Zero ⓘ

Ioannis Antonoglou → coAuthorOf → Mastering the game of Go with deep neural networks and tree search ⓘ

linked to: AlphaGo Zero

Ioannis Antonoglou → coAuthorOf → Mastering the game of Go without human knowledge ⓘ

linked to: AlphaGo Zero

AlphaGo Zero → describedInPublication → Mastering the game of Go without human knowledge ⓘ

linked to: AlphaGo Zero

Thomas Hubert → notableWork → AlphaGo Zero ⓘ

Lucas Baker → notableWorkTopic → AlphaGo Zero ⓘ

Matthew Lai → knownFor → AlphaGo Zero ⓘ

Matthew Lai → notableWork → AlphaGo Zero ⓘ

John Nham → hasSubjectOfWriting → AlphaGo Zero ⓘ

Pushmeet Kohli → notableWork → AlphaGo Zero ⓘ

All labels observed (3)

How this entity was disambiguated Show

Statements (52)

How these facts were elicited Show

Referenced by (12)

How this entity was disambiguated

How these facts were elicited