AlphaZero
E40166
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.
All labels observed (6)
How this entity was disambiguated
This entity first appeared as the object of triple T307332 — 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: AlphaZero Context triple: [DeepMind, knownFor, AlphaZero]
-
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.
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B.
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.
-
C.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
- 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: AlphaZero Target entity description: 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.
-
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.
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.
-
C.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
- F. None of above. chosen
Statements (53)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence system
ⓘ
game‑playing program ⓘ |
| architectureType | deep neural network with Monte Carlo tree search ⓘ |
| basedOn |
Monte Carlo tree search
ⓘ
deep learning ⓘ reinforcement learning ⓘ |
| contrastWith |
programs relying on human expert knowledge
ⓘ
traditional chess engines using alpha‑beta search ⓘ |
| countryOfOrigin | United Kingdom ⓘ |
| creatorOrganizationType | AI research lab ⓘ |
| defeated |
Elmo shogi engine
ⓘ
Stockfish ⓘ
surface form:
Stockfish 8
previous Go programs based on AlphaGo Zero ⓘ |
| designedFor |
Go
ⓘ
chess ⓘ shogi ⓘ |
| developer |
DeepMind
ⓘ
DeepMind ⓘ
surface form:
Google DeepMind
|
| doesNotUse |
endgame tablebases for search guidance
ⓘ
human‑crafted opening books ⓘ |
| evaluationFunction | learned value function ⓘ |
| field |
artificial intelligence
ⓘ
computer Go ⓘ computer chess ⓘ computer shogi ⓘ machine learning ⓘ |
| firstPublicAnnouncementDate | 2017-12-06 ⓘ |
| firstPublicAnnouncementYear | 2017 ⓘ |
| gameRepresentation | board positions encoded for neural networks ⓘ |
| generalizationProperty | single algorithm applied to multiple games ⓘ |
| hardwareUsed |
TPUs (via XLA integrations)
ⓘ
surface form:
TPUs
|
| learningObjective | maximize expected game outcome ⓘ |
| learningParadigm | tabula rasa learning ⓘ |
| notableFor |
mastering Go through self‑play
ⓘ
mastering chess through self‑play ⓘ mastering shogi through self‑play ⓘ |
| outperforms |
AlphaGo Zero
ⓘ
Elmo ⓘ Stockfish ⓘ |
| parentProject |
AlphaGo
ⓘ
surface form:
AlphaGo project
|
| policyRepresentation | probability distribution over moves ⓘ |
| publicationTitle |
AlphaZero
self-linksurface differs
ⓘ
surface form:
A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play
|
| publishedIn | Science ⓘ |
| rewardSignal | game result win‑draw‑loss ⓘ |
| searchGuidance |
policy network priors
ⓘ
value network evaluations ⓘ |
| searchTechnique | Monte Carlo tree search guided by neural networks ⓘ |
| trainingDataSource | self‑generated game data ⓘ |
| trainingMethod | self‑play ⓘ |
| trainingRegime | self‑play reinforcement learning without human examples ⓘ |
| uses |
neural networks
ⓘ
policy network ⓘ value network ⓘ |
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: AlphaZero Description of subject: 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.
Referenced by (20)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
this entity surface form:
A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play
this entity surface form:
DeepMind AlphaZero
this entity surface form:
AlphaZero (shogi) in DeepMind experiments