DPLL(T)
E904156
DPLL(T) is a framework that extends the classic DPLL SAT-solving algorithm with theory solvers to efficiently decide satisfiability modulo background theories such as arithmetic, arrays, or bit-vectors.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DPLL(T) canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T11090175 — 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: DPLL(T) Context triple: [Satisfiability Modulo Theories, typicalArchitecture, DPLL(T)]
-
A.
Satisfiability Modulo Theories (SMT)
Satisfiability Modulo Theories (SMT) is a framework in computer science and mathematical logic for deciding the satisfiability of logical formulas with respect to background theories such as arithmetic, bit-vectors, arrays, and data types, widely used in verification, synthesis, and automated reasoning.
-
B.
Z3: An Efficient SMT Solver
Z3: An Efficient SMT Solver is a high-performance satisfiability modulo theories (SMT) solver widely used in program verification, formal methods, and automated reasoning.
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C.
CDCL SAT solver
A CDCL SAT solver is an advanced algorithm for solving Boolean satisfiability problems that extends the classic DPLL approach with conflict-driven clause learning and non-chronological backtracking to greatly improve efficiency on large, complex instances.
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D.
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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E.
Vampire automated theorem prover
Vampire automated theorem prover is a high-performance first-order logic reasoning system widely used in automated deduction and formal verification research.
- 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: DPLL(T) Target entity description: DPLL(T) is a framework that extends the classic DPLL SAT-solving algorithm with theory solvers to efficiently decide satisfiability modulo background theories such as arithmetic, arrays, or bit-vectors.
-
A.
Satisfiability Modulo Theories (SMT)
Satisfiability Modulo Theories (SMT) is a framework in computer science and mathematical logic for deciding the satisfiability of logical formulas with respect to background theories such as arithmetic, bit-vectors, arrays, and data types, widely used in verification, synthesis, and automated reasoning.
-
B.
Z3: An Efficient SMT Solver
Z3: An Efficient SMT Solver is a high-performance satisfiability modulo theories (SMT) solver widely used in program verification, formal methods, and automated reasoning.
-
C.
CDCL SAT solver
A CDCL SAT solver is an advanced algorithm for solving Boolean satisfiability problems that extends the classic DPLL approach with conflict-driven clause learning and non-chronological backtracking to greatly improve efficiency on large, complex instances.
-
D.
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.
-
E.
Vampire automated theorem prover
Vampire automated theorem prover is a high-performance first-order logic reasoning system widely used in automated deduction and formal verification research.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
SMT solving framework
ⓘ
algorithmic framework ⓘ decision procedure framework ⓘ |
| abbreviationOf | Davis–Putnam–Logemann–Loveland with Theories NERFINISHED ⓘ |
| basedOn | DPLL NERFINISHED ⓘ |
| checks | theory consistency of partial assignments ⓘ |
| combines |
Boolean reasoning
ⓘ
theory reasoning ⓘ |
| communicatesVia | interface between SAT solver and theory solver ⓘ |
| designedFor | Satisfiability Modulo Theories NERFINISHED ⓘ |
| enables |
incremental theory reasoning
ⓘ
lazy theory instantiation ⓘ |
| extends | DPLL SAT-solving algorithm NERFINISHED ⓘ |
| goal | efficient decision procedures for SMT problems ⓘ |
| hasComponent |
Boolean search engine
ⓘ
clause learning ⓘ conflict analysis ⓘ theory consistency checking ⓘ theory propagation ⓘ |
| improvesOn | pure SAT-based decision procedures for rich theories ⓘ |
| influenced | modern SMT solver architectures ⓘ |
| operatesOn | Boolean abstraction of theory atoms ⓘ |
| originField |
automated reasoning
ⓘ
formal methods ⓘ |
| relatedTo |
CDCL
ⓘ
Nelson–Oppen combination method NERFINISHED ⓘ |
| requires | theory solver for each background theory ⓘ |
| supports |
arithmetic theories
ⓘ
arrays ⓘ background theories ⓘ bit-vectors ⓘ combinations of theories ⓘ linear arithmetic ⓘ uninterpreted functions ⓘ |
| typicalImplementationInvolves |
backjumping
GENERATED
ⓘ
conflict-driven clause learning GENERATED ⓘ unit propagation GENERATED ⓘ |
| usedIn |
SMT-LIB compliant solvers
ⓘ
Satisfiability Modulo Theories solvers NERFINISHED ⓘ hardware verification ⓘ model checking ⓘ program analysis ⓘ software verification ⓘ |
| uses |
SAT solver
NERFINISHED
ⓘ
theory solver ⓘ |
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: DPLL(T) Description of subject: DPLL(T) is a framework that extends the classic DPLL SAT-solving algorithm with theory solvers to efficiently decide satisfiability modulo background theories such as arithmetic, arrays, or bit-vectors.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Satisfiability Modulo Theories
subject surface form:
Satisfiability Modulo Theories