“Probabilistic computations: Toward a unified measure of complexity”
E926127
“Probabilistic computations: Toward a unified measure of complexity” is a seminal research paper by Andrew Yao that laid foundational concepts in computational complexity theory, particularly regarding the role and analysis of randomness in algorithms.
All labels observed (1)
| Label | Occurrences |
|---|---|
| “Probabilistic computations: Toward a unified measure of complexity” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11438094 — 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: “Probabilistic computations: Toward a unified measure of complexity” Context triple: [Andrew Yao, notableWork, “Probabilistic computations: Toward a unified measure of complexity”]
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A.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
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B.
Computational Complexity: A Conceptual Perspective
Computational Complexity: A Conceptual Perspective is a graduate-level textbook that presents the foundations and key themes of computational complexity theory with an emphasis on conceptual understanding over technical detail.
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C.
Randomness and Computation
"Randomness and Computation" is Shafi Goldwasser's influential doctoral thesis that helped lay the foundations of modern complexity theory and cryptography by rigorously exploring the role of randomness in efficient computation.
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D.
Kolmogorov complexity
Kolmogorov complexity is a measure of the amount of information in an object, defined as the length of the shortest computer program that can produce it.
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E.
P, NP, and NP-Completeness: The Basics of Complexity Theory
"P, NP, and NP-Completeness: The Basics of Complexity Theory" is a foundational textbook by Oded Goldreich that introduces the core concepts, problems, and techniques of computational complexity theory, with a focus on the classes P, NP, and NP-complete problems.
- 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: “Probabilistic computations: Toward a unified measure of complexity” Target entity description: “Probabilistic computations: Toward a unified measure of complexity” is a seminal research paper by Andrew Yao that laid foundational concepts in computational complexity theory, particularly regarding the role and analysis of randomness in algorithms.
-
A.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
-
B.
Computational Complexity: A Conceptual Perspective
Computational Complexity: A Conceptual Perspective is a graduate-level textbook that presents the foundations and key themes of computational complexity theory with an emphasis on conceptual understanding over technical detail.
-
C.
Randomness and Computation
"Randomness and Computation" is Shafi Goldwasser's influential doctoral thesis that helped lay the foundations of modern complexity theory and cryptography by rigorously exploring the role of randomness in efficient computation.
-
D.
Kolmogorov complexity
Kolmogorov complexity is a measure of the amount of information in an object, defined as the length of the shortest computer program that can produce it.
-
E.
P, NP, and NP-Completeness: The Basics of Complexity Theory
"P, NP, and NP-Completeness: The Basics of Complexity Theory" is a foundational textbook by Oded Goldreich that introduces the core concepts, problems, and techniques of computational complexity theory, with a focus on the classes P, NP, and NP-complete problems.
- F. None of above. chosen
Statements (35)
| Predicate | Object |
|---|---|
| instanceOf |
research paper
ⓘ
scientific article ⓘ |
| author |
Andrew Chi-Chih Yao
NERFINISHED
ⓘ
Andrew Yao NERFINISHED ⓘ |
| citedFor |
formal treatment of probabilistic computation models
ⓘ
foundational role in the theory of randomized algorithms ⓘ framework for comparing probabilistic and deterministic algorithms ⓘ |
| contribution |
analyzed the power of probabilistic algorithms compared to deterministic algorithms
ⓘ
formalized the role of randomness in computational complexity ⓘ helped define and clarify probabilistic complexity classes ⓘ introduced a unified framework for measuring the complexity of probabilistic computations ⓘ |
| describedAs | a seminal research paper that laid foundational concepts in computational complexity theory regarding randomness in algorithms ⓘ |
| field |
computational complexity theory
ⓘ
computer science ⓘ theoretical computer science ⓘ |
| hasShortTitle | Probabilistic computations NERFINISHED ⓘ |
| hasTitle | Probabilistic computations: Toward a unified measure of complexity NERFINISHED ⓘ |
| impact |
considered a seminal work in computational complexity theory
ⓘ
influenced later research on randomized algorithms ⓘ influenced the study of the relationship between deterministic and probabilistic computation ⓘ |
| influencedBy | earlier work on Turing machines and complexity measures ⓘ |
| language | English ⓘ |
| relatedTo |
algorithm analysis
ⓘ
deterministic complexity theory ⓘ randomized complexity classes such as RP and BPP ⓘ |
| studies |
probabilistic models of computation
ⓘ
the complexity of algorithms that use randomization ⓘ |
| topic |
complexity classes
ⓘ
probabilistic Turing machines ⓘ probabilistic computation ⓘ randomized algorithms ⓘ randomness in computation ⓘ |
| usedIn |
graduate-level courses in computational complexity
ⓘ
research on complexity class separations ⓘ research on derandomization ⓘ |
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: “Probabilistic computations: Toward a unified measure of complexity” Description of subject: “Probabilistic computations: Toward a unified measure of complexity” is a seminal research paper by Andrew Yao that laid foundational concepts in computational complexity theory, particularly regarding the role and analysis of randomness in algorithms.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.