General Problem Solver
E267834
The General Problem Solver is an early artificial intelligence program designed to model and automate human-like problem-solving across a wide range of domains using general search and reasoning strategies.
All labels observed (4)
| Label | Occurrences |
|---|---|
| General Problem Solver canonical | 5 |
| AI planning | 1 |
| Newell and Simon's theory of problem solving | 1 |
| the General Problem Solver (GPS) | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2455504 — 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: General Problem Solver Context triple: [Human Problem Solving, buildsOn, General Problem Solver]
-
A.
Human Problem Solving
"Human Problem Solving" is a seminal 1972 book by Allen Newell and Herbert A. Simon that presents a foundational cognitive science and artificial intelligence theory of how humans represent and solve complex problems.
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B.
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" is the seminal 1955 research proposal by John McCarthy and colleagues that launched the field of artificial intelligence by defining its goals and organizing the landmark 1956 Dartmouth conference.
-
C.
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.
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D.
Entscheidungsproblem
The Entscheidungsproblem is a foundational decision problem in mathematical logic that asks whether there exists a general algorithm to determine the truth or falsity of any given first-order logical statement.
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E.
Turing test
The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
- 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: General Problem Solver Target entity description: The General Problem Solver is an early artificial intelligence program designed to model and automate human-like problem-solving across a wide range of domains using general search and reasoning strategies.
-
A.
Human Problem Solving
"Human Problem Solving" is a seminal 1972 book by Allen Newell and Herbert A. Simon that presents a foundational cognitive science and artificial intelligence theory of how humans represent and solve complex problems.
-
B.
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"
"A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" is the seminal 1955 research proposal by John McCarthy and colleagues that launched the field of artificial intelligence by defining its goals and organizing the landmark 1956 Dartmouth conference.
-
C.
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.
-
D.
Entscheidungsproblem
The Entscheidungsproblem is a foundational decision problem in mathematical logic that asks whether there exists a general algorithm to determine the truth or falsity of any given first-order logical statement.
-
E.
Turing test
The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence program
ⓘ
computer program ⓘ problem-solving system ⓘ symbolic AI system ⓘ |
| aimedToModel |
human problem solving
ⓘ
human-like reasoning ⓘ |
| alsoKnownAs | GPS ⓘ |
| basedOn | means-ends analysis ⓘ |
| coreConcept |
difference-reducing search
ⓘ
goal-subgoal decomposition ⓘ problem space representation ⓘ production-like operators ⓘ |
| countryOfOrigin |
United States of America
ⓘ
surface form:
United States
|
| designedFor |
domain-independent problem solving
ⓘ
general problem solving ⓘ logic problems ⓘ puzzle solving ⓘ symbolic reasoning tasks ⓘ theorem proving ⓘ |
| developedAt |
CMU
ⓘ
surface form:
Carnegie Mellon University
RAND Corporation ⓘ |
| developer |
Allen Newell
ⓘ
Herbert Simon ⓘ
surface form:
Herbert A. Simon
J. C. Shaw ⓘ |
| documentedIn | Newell and Simon's publications on human problem solving ⓘ |
| field |
artificial intelligence
ⓘ
cognitive science ⓘ |
| historicalPeriod | early AI era ⓘ |
| inception |
1957
ⓘ
late 1950s ⓘ |
| influenced |
cognitive psychology models of problem solving
ⓘ
general search models in AI ⓘ physical symbol system hypothesis ⓘ production system architectures ⓘ |
| influencedBy |
the Logic Theorist program
ⓘ
surface form:
Logic Theorist
early symbolic logic programs ⓘ |
| limitation |
computationally expensive search
ⓘ
effective only on well-structured problems ⓘ requires formal symbolic problem representation ⓘ |
| notableFor |
being one of the first general-purpose AI programs
ⓘ
explicitly separating problem-solving strategy from problem content ⓘ formalizing means-ends analysis ⓘ |
| programmingLanguage | Information Processing Language ⓘ |
| status | historical AI system ⓘ |
| usesMethod |
goal-directed reasoning
ⓘ
heuristic search ⓘ means-ends analysis ⓘ operator application ⓘ problem reduction ⓘ state-space search ⓘ |
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: General Problem Solver Description of subject: The General Problem Solver is an early artificial intelligence program designed to model and automate human-like problem-solving across a wide range of domains using general search and reasoning strategies.
Referenced by (8)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
the General Problem Solver (GPS)
subject surface form:
Programs with Common Sense
this entity surface form:
AI planning
subject surface form:
Logic Theorist
subject surface form:
Logic Theorist
this entity surface form:
Newell and Simon's theory of problem solving