SlideShare a Scribd company logo
The solver
David Déharbe, Pablo Federico Dobal,
Pascal Fontaine, . . .
VeriDis
4 octobre 2013 1 / 7
SAT and SMT solvers : what can they do for you
Automated reasoning, constraint solving
Reason on (reasonably large) sets of facts
Depending on the facts, more or less expressivity is required
E.g. Sudokus can be expressed in SAT logic
Besides solving puzzles, SAT/SMT solvers are used for :
planning
configuration checking
placement
model checking
formal verification
. . .
4 octobre 2013 2 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” :
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” : p1,1,1
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” : p1,1,1
Line 2, column 4, there is “3” :
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” : p1,1,1
Line 2, column 4, there is “3” : p2,4,3
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” : p1,1,1
Line 2, column 4, there is “3” : p2,4,3
At position (1, 2), there is only one number
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
Line 1, column 1, there is “1” : p1,1,1
Line 2, column 4, there is “3” : p2,4,3
At position (1, 2), there is only one number
¬p1,2,1 ∨ ¬p1,2,2
¬p1,2,1 ∨ ¬p1,2,3
¬p1,2,1 ∨ ¬p1,2,4
¬p1,2,2 ∨ ¬p1,2,3
¬p1,2,2 ∨ ¬p1,2,4
¬p1,2,3 ∨ ¬p1,2,4
4 octobre 2013 3 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4
Number “1” should be somewhere on line 1
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4
Number “1” should be somewhere on line 1
p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4
Number “1” should be somewhere on line 1
p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1
Number “1” should be at most once on line 1
4 octobre 2013 4 / 7
Using a SAT solver : a toy example
1
3
2
2
“or” : ∨
“and” : ∧
negation : ¬
line i, column j, there is x (pi,j,x)
At position (1, 2), there is either 1, 2, 3, or 4
p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4
Number “1” should be somewhere on line 1
p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1
Number “1” should be at most once on line 1
¬p1,1,1 ∨ ¬p1,2,1
¬p1,1,1 ∨ ¬p1,3,1
¬p1,1,1 ∨ ¬p1,4,1
¬p1,2,1 ∨ ¬p1,3,1
¬p1,2,1 ∨ ¬p1,4,1
¬p1,3,1 ∨ ¬p1,4,1
4 octobre 2013 4 / 7
SMT = SAT + expressiveness
SAT solvers
¬ (p ⇒ q) ⇒ (¬p ⇒ q) ⇒ q
Congruence closure (uninterpreted symbols + equality)
a = b ∧ f(a) = f(b) ∨ (p(a) ∧ ¬p(b))
adding arithmetic
a ≤ b ∧ b ≤ a + x ∧ x = 0 ∧ f(a) = f(b) ∨ (p(a) ∧ ¬p(b + x))
. . .
Some examples : Barcelogic, CVC4 (SVC, CVC, CVC-lite, CVC3),
MathSAT, OpenSMT, Yices, Z3 . . .
The solver
4 octobre 2013 5 / 7
SMT internals
SMT formula
SMT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
SAT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Boolean Model
SAT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Boolean Model
Theory
reasoner
SAT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Boolean Model
Conflict clause
Theory
reasoner
SAT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Quantifier-free SMT solver
Boolean Model
Conflict clause
Theory
reasoner
SAT solver
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Quantifier-free SMT solver
Boolean Model
Conflict clause
Theory
reasoner
SAT solver
Model
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Quantifier-free SMT solver
Boolean Model
Conflict clause
Theory
reasoner
SAT solver
Instantiation
module
Instance
Model
4 octobre 2013 6 / 7
SMT internals
SMT formula
SMT solver
Quantifier-free SMT solver
Boolean Model
Conflict clause
Theory
reasoner
SAT solver
Instantiation
module
Instance
Model
Model UNSAT (proof/core)
4 octobre 2013 6 / 7
Conclusion
Reasoning on large formulas with arithmetic, functions, equalities,. . .
Applications
planning
placement
bounded model-checking
verification
. . .
The solver
http://www.verit-solver.org
BSD license
4 octobre 2013 7 / 7

More Related Content

What's hot

Vertex cover Problem
Vertex cover ProblemVertex cover Problem
Vertex cover Problem
Gajanand Sharma
 
Mathematical Statistics Homework Help
Mathematical Statistics Homework HelpMathematical Statistics Homework Help
Mathematical Statistics Homework Help
Statistics Homework Helper
 
Algorithm Assignment Help
Algorithm Assignment HelpAlgorithm Assignment Help
Algorithm Assignment Help
Programming Homework Help
 
SRS presentation - Stanley Depth
SRS presentation - Stanley DepthSRS presentation - Stanley Depth
SRS presentation - Stanley Depth
AJ Joshi
 
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
anasKhalaf4
 
Enumeration of 2-level polytopes
Enumeration of 2-level polytopesEnumeration of 2-level polytopes
Enumeration of 2-level polytopes
Vissarion Fisikopoulos
 
Stdlib functions lesson
Stdlib functions lessonStdlib functions lesson
Stdlib functions lesson
teach4uin
 
Math 4 graphing rational functions
Math 4 graphing rational functionsMath 4 graphing rational functions
Math 4 graphing rational functions
Leo Crisologo
 
Ch04
Ch04Ch04
Ch04
Hankyo
 
3.4 looking for real roots of real polynomials t
3.4 looking for real roots of real polynomials t3.4 looking for real roots of real polynomials t
3.4 looking for real roots of real polynomials t
math260
 
Logarithms
LogarithmsLogarithms
Logarithms
supoteta
 
Polycycles and their elementary decompositions
Polycycles and their elementary decompositionsPolycycles and their elementary decompositions
Polycycles and their elementary decompositions
Mathieu Dutour Sikiric
 
1.5 algebraic and elementary functions
1.5 algebraic and elementary functions1.5 algebraic and elementary functions
1.5 algebraic and elementary functions
math265
 
Loss Calibrated Variational Inference
Loss Calibrated Variational InferenceLoss Calibrated Variational Inference
Loss Calibrated Variational Inference
Tomasz Kusmierczyk
 
Polynomial functions
Polynomial functionsPolynomial functions
Polynomial functions
Leo Crisologo
 
1.3 sign charts and inequalities
1.3 sign charts and inequalities1.3 sign charts and inequalities
1.3 sign charts and inequalities
math123c
 

What's hot (16)

Vertex cover Problem
Vertex cover ProblemVertex cover Problem
Vertex cover Problem
 
Mathematical Statistics Homework Help
Mathematical Statistics Homework HelpMathematical Statistics Homework Help
Mathematical Statistics Homework Help
 
Algorithm Assignment Help
Algorithm Assignment HelpAlgorithm Assignment Help
Algorithm Assignment Help
 
SRS presentation - Stanley Depth
SRS presentation - Stanley DepthSRS presentation - Stanley Depth
SRS presentation - Stanley Depth
 
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022 ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
ملزمة الرياضيات للصف السادس التطبيقي الفصل الاول الاعداد المركبة 2022
 
Enumeration of 2-level polytopes
Enumeration of 2-level polytopesEnumeration of 2-level polytopes
Enumeration of 2-level polytopes
 
Stdlib functions lesson
Stdlib functions lessonStdlib functions lesson
Stdlib functions lesson
 
Math 4 graphing rational functions
Math 4 graphing rational functionsMath 4 graphing rational functions
Math 4 graphing rational functions
 
Ch04
Ch04Ch04
Ch04
 
3.4 looking for real roots of real polynomials t
3.4 looking for real roots of real polynomials t3.4 looking for real roots of real polynomials t
3.4 looking for real roots of real polynomials t
 
Logarithms
LogarithmsLogarithms
Logarithms
 
Polycycles and their elementary decompositions
Polycycles and their elementary decompositionsPolycycles and their elementary decompositions
Polycycles and their elementary decompositions
 
1.5 algebraic and elementary functions
1.5 algebraic and elementary functions1.5 algebraic and elementary functions
1.5 algebraic and elementary functions
 
Loss Calibrated Variational Inference
Loss Calibrated Variational InferenceLoss Calibrated Variational Inference
Loss Calibrated Variational Inference
 
Polynomial functions
Polynomial functionsPolynomial functions
Polynomial functions
 
1.3 sign charts and inequalities
1.3 sign charts and inequalities1.3 sign charts and inequalities
1.3 sign charts and inequalities
 

Viewers also liked

GENERATION Y ET UNIVERSITE 2.0
GENERATION Y ET UNIVERSITE 2.0GENERATION Y ET UNIVERSITE 2.0
GENERATION Y ET UNIVERSITE 2.0
Prof. Jacques Folon (Ph.D)
 
3574 Med 0911 Afb Depliant A
3574 Med 0911 Afb Depliant A3574 Med 0911 Afb Depliant A
3574 Med 0911 Afb Depliant A
jm h
 
PNC Branch Bank
PNC Branch BankPNC Branch Bank
PNC Branch Bank
Allen Angle
 
Curso contaminación atmosférica y acústica
Curso contaminación atmosférica y acústicaCurso contaminación atmosférica y acústica
Curso contaminación atmosférica y acústica
Euroinnova Formación
 
Le mercure dans les ampoules à basse consommation
Le mercure dans les ampoules à basse consommationLe mercure dans les ampoules à basse consommation
Le mercure dans les ampoules à basse consommation
GreenFacts
 

Viewers also liked (7)

GENERATION Y ET UNIVERSITE 2.0
GENERATION Y ET UNIVERSITE 2.0GENERATION Y ET UNIVERSITE 2.0
GENERATION Y ET UNIVERSITE 2.0
 
3574 Med 0911 Afb Depliant A
3574 Med 0911 Afb Depliant A3574 Med 0911 Afb Depliant A
3574 Med 0911 Afb Depliant A
 
Venezia
VeneziaVenezia
Venezia
 
La Meuse 121027
La Meuse 121027La Meuse 121027
La Meuse 121027
 
PNC Branch Bank
PNC Branch BankPNC Branch Bank
PNC Branch Bank
 
Curso contaminación atmosférica y acústica
Curso contaminación atmosférica y acústicaCurso contaminación atmosférica y acústica
Curso contaminación atmosférica y acústica
 
Le mercure dans les ampoules à basse consommation
Le mercure dans les ampoules à basse consommationLe mercure dans les ampoules à basse consommation
Le mercure dans les ampoules à basse consommation
 

Similar to Owf 2013 rii veri t fontaine speaker4

PRP - Unit 1.pptx
PRP - Unit 1.pptxPRP - Unit 1.pptx
PRP - Unit 1.pptx
AryanArora64
 
Asymptote Curve I
Asymptote Curve IAsymptote Curve I
Asymptote Curve I
Hirwanto Iwan
 
Introduction to probability solutions manual
Introduction to probability   solutions manualIntroduction to probability   solutions manual
Introduction to probability solutions manual
Kibria Prangon
 
Lecture-1-Probability-Theory-Part-1.pdf
Lecture-1-Probability-Theory-Part-1.pdfLecture-1-Probability-Theory-Part-1.pdf
Lecture-1-Probability-Theory-Part-1.pdf
MICAHJAMELLEICAWAT1
 
Orthogonal basis and gram schmidth process
Orthogonal basis and gram schmidth processOrthogonal basis and gram schmidth process
Orthogonal basis and gram schmidth process
gidc engineering college
 
Fear and loathing with APL (oredev)
Fear and loathing with APL (oredev)Fear and loathing with APL (oredev)
Fear and loathing with APL (oredev)
Yan Cui
 
PROBABILITY THEORIES.pptx
PROBABILITY THEORIES.pptxPROBABILITY THEORIES.pptx
PROBABILITY THEORIES.pptx
MarcJoshuaGorospe
 
Discrete Math IP4 - Automata Theory
Discrete Math IP4 - Automata TheoryDiscrete Math IP4 - Automata Theory
Discrete Math IP4 - Automata Theory
Mark Simon
 
Heuristics slides 321
Heuristics slides 321Heuristics slides 321
Heuristics slides 321
Moe Tmz
 
Heuristics
HeuristicsHeuristics
Heuristics
Moe Tmz
 
Lect14
Lect14Lect14
Lect14
sukh_reet
 
pushdown automata
pushdown automatapushdown automata
pushdown automata
Sujata Pardeshi
 
Ip 5 discrete mathematics
Ip 5 discrete mathematicsIp 5 discrete mathematics
Ip 5 discrete mathematics
Mark Simon
 
Discrete mathematic question answers
Discrete mathematic question answersDiscrete mathematic question answers
Discrete mathematic question answers
Samet öztoprak
 
Discrete probability
Discrete probabilityDiscrete probability
Discrete probability
Ranjan Kumar
 
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHMBIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
ijcsa
 
11 x1 t15 05 polynomial results (2013)
11 x1 t15 05 polynomial results (2013)11 x1 t15 05 polynomial results (2013)
11 x1 t15 05 polynomial results (2013)
Nigel Simmons
 
Algebra 2 Section 2-5
Algebra 2 Section 2-5Algebra 2 Section 2-5
Algebra 2 Section 2-5
Jimbo Lamb
 
Mathematics
MathematicsMathematics
Mathematics
MathematicsMathematics
Mathematics
jiteshtuteja
 

Similar to Owf 2013 rii veri t fontaine speaker4 (20)

PRP - Unit 1.pptx
PRP - Unit 1.pptxPRP - Unit 1.pptx
PRP - Unit 1.pptx
 
Asymptote Curve I
Asymptote Curve IAsymptote Curve I
Asymptote Curve I
 
Introduction to probability solutions manual
Introduction to probability   solutions manualIntroduction to probability   solutions manual
Introduction to probability solutions manual
 
Lecture-1-Probability-Theory-Part-1.pdf
Lecture-1-Probability-Theory-Part-1.pdfLecture-1-Probability-Theory-Part-1.pdf
Lecture-1-Probability-Theory-Part-1.pdf
 
Orthogonal basis and gram schmidth process
Orthogonal basis and gram schmidth processOrthogonal basis and gram schmidth process
Orthogonal basis and gram schmidth process
 
Fear and loathing with APL (oredev)
Fear and loathing with APL (oredev)Fear and loathing with APL (oredev)
Fear and loathing with APL (oredev)
 
PROBABILITY THEORIES.pptx
PROBABILITY THEORIES.pptxPROBABILITY THEORIES.pptx
PROBABILITY THEORIES.pptx
 
Discrete Math IP4 - Automata Theory
Discrete Math IP4 - Automata TheoryDiscrete Math IP4 - Automata Theory
Discrete Math IP4 - Automata Theory
 
Heuristics slides 321
Heuristics slides 321Heuristics slides 321
Heuristics slides 321
 
Heuristics
HeuristicsHeuristics
Heuristics
 
Lect14
Lect14Lect14
Lect14
 
pushdown automata
pushdown automatapushdown automata
pushdown automata
 
Ip 5 discrete mathematics
Ip 5 discrete mathematicsIp 5 discrete mathematics
Ip 5 discrete mathematics
 
Discrete mathematic question answers
Discrete mathematic question answersDiscrete mathematic question answers
Discrete mathematic question answers
 
Discrete probability
Discrete probabilityDiscrete probability
Discrete probability
 
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHMBIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
BIN PACKING PROBLEM: A LINEAR CONSTANTSPACE  -APPROXIMATION ALGORITHM
 
11 x1 t15 05 polynomial results (2013)
11 x1 t15 05 polynomial results (2013)11 x1 t15 05 polynomial results (2013)
11 x1 t15 05 polynomial results (2013)
 
Algebra 2 Section 2-5
Algebra 2 Section 2-5Algebra 2 Section 2-5
Algebra 2 Section 2-5
 
Mathematics
MathematicsMathematics
Mathematics
 
Mathematics
MathematicsMathematics
Mathematics
 

More from Patrick MOREAU

Aristote reproductibilite des resultats
Aristote reproductibilite des resultatsAristote reproductibilite des resultats
Aristote reproductibilite des resultats
Patrick MOREAU
 
Captronic grenoble 01102014 version presentee
Captronic grenoble 01102014 version presenteeCaptronic grenoble 01102014 version presentee
Captronic grenoble 01102014 version presentee
Patrick MOREAU
 
Owf 2013 rii why paskevich speaker3
Owf 2013 rii why paskevich speaker3Owf 2013 rii why paskevich speaker3
Owf 2013 rii why paskevich speaker3
Patrick MOREAU
 
Owf 2013 rii panorama leroy
Owf 2013 rii panorama leroyOwf 2013 rii panorama leroy
Owf 2013 rii panorama leroyPatrick MOREAU
 
Owf 2013 rii intro moreau
Owf 2013 rii intro moreauOwf 2013 rii intro moreau
Owf 2013 rii intro moreauPatrick MOREAU
 
Owf 2013 rii coccinelle muller speaker 1
Owf 2013 rii coccinelle muller speaker 1Owf 2013 rii coccinelle muller speaker 1
Owf 2013 rii coccinelle muller speaker 1
Patrick MOREAU
 
Owf 2013 rii alter go speaker 5
Owf 2013 rii alter go speaker 5 Owf 2013 rii alter go speaker 5
Owf 2013 rii alter go speaker 5
Patrick MOREAU
 
Owf 2013 rii moose speaker 2
Owf 2013 rii moose speaker 2Owf 2013 rii moose speaker 2
Owf 2013 rii moose speaker 2
Patrick MOREAU
 

More from Patrick MOREAU (8)

Aristote reproductibilite des resultats
Aristote reproductibilite des resultatsAristote reproductibilite des resultats
Aristote reproductibilite des resultats
 
Captronic grenoble 01102014 version presentee
Captronic grenoble 01102014 version presenteeCaptronic grenoble 01102014 version presentee
Captronic grenoble 01102014 version presentee
 
Owf 2013 rii why paskevich speaker3
Owf 2013 rii why paskevich speaker3Owf 2013 rii why paskevich speaker3
Owf 2013 rii why paskevich speaker3
 
Owf 2013 rii panorama leroy
Owf 2013 rii panorama leroyOwf 2013 rii panorama leroy
Owf 2013 rii panorama leroy
 
Owf 2013 rii intro moreau
Owf 2013 rii intro moreauOwf 2013 rii intro moreau
Owf 2013 rii intro moreau
 
Owf 2013 rii coccinelle muller speaker 1
Owf 2013 rii coccinelle muller speaker 1Owf 2013 rii coccinelle muller speaker 1
Owf 2013 rii coccinelle muller speaker 1
 
Owf 2013 rii alter go speaker 5
Owf 2013 rii alter go speaker 5 Owf 2013 rii alter go speaker 5
Owf 2013 rii alter go speaker 5
 
Owf 2013 rii moose speaker 2
Owf 2013 rii moose speaker 2Owf 2013 rii moose speaker 2
Owf 2013 rii moose speaker 2
 

Recently uploaded

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Alex Pruden
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
AstuteBusiness
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Neo4j
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
BibashShahi
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
Safe Software
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Neo4j
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSFAppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
Ajin Abraham
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
saastr
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
operationspcvita
 

Recently uploaded (20)

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge GraphGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
 
Artificial Intelligence and Electronic Warfare
Artificial Intelligence and Electronic WarfareArtificial Intelligence and Electronic Warfare
Artificial Intelligence and Electronic Warfare
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSFAppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
 

Owf 2013 rii veri t fontaine speaker4

  • 1. The solver David Déharbe, Pablo Federico Dobal, Pascal Fontaine, . . . VeriDis 4 octobre 2013 1 / 7
  • 2. SAT and SMT solvers : what can they do for you Automated reasoning, constraint solving Reason on (reasonably large) sets of facts Depending on the facts, more or less expressivity is required E.g. Sudokus can be expressed in SAT logic Besides solving puzzles, SAT/SMT solvers are used for : planning configuration checking placement model checking formal verification . . . 4 octobre 2013 2 / 7
  • 3. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) 4 octobre 2013 3 / 7
  • 4. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : 4 octobre 2013 3 / 7
  • 5. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : p1,1,1 4 octobre 2013 3 / 7
  • 6. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : p1,1,1 Line 2, column 4, there is “3” : 4 octobre 2013 3 / 7
  • 7. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : p1,1,1 Line 2, column 4, there is “3” : p2,4,3 4 octobre 2013 3 / 7
  • 8. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : p1,1,1 Line 2, column 4, there is “3” : p2,4,3 At position (1, 2), there is only one number 4 octobre 2013 3 / 7
  • 9. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) Line 1, column 1, there is “1” : p1,1,1 Line 2, column 4, there is “3” : p2,4,3 At position (1, 2), there is only one number ¬p1,2,1 ∨ ¬p1,2,2 ¬p1,2,1 ∨ ¬p1,2,3 ¬p1,2,1 ∨ ¬p1,2,4 ¬p1,2,2 ∨ ¬p1,2,3 ¬p1,2,2 ∨ ¬p1,2,4 ¬p1,2,3 ∨ ¬p1,2,4 4 octobre 2013 3 / 7
  • 10. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) 4 octobre 2013 4 / 7
  • 11. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 4 octobre 2013 4 / 7
  • 12. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4 4 octobre 2013 4 / 7
  • 13. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4 Number “1” should be somewhere on line 1 4 octobre 2013 4 / 7
  • 14. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4 Number “1” should be somewhere on line 1 p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1 4 octobre 2013 4 / 7
  • 15. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4 Number “1” should be somewhere on line 1 p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1 Number “1” should be at most once on line 1 4 octobre 2013 4 / 7
  • 16. Using a SAT solver : a toy example 1 3 2 2 “or” : ∨ “and” : ∧ negation : ¬ line i, column j, there is x (pi,j,x) At position (1, 2), there is either 1, 2, 3, or 4 p1,2,1 ∨ p1,2,2 ∨ p1,2,3 ∨ p1,2,4 Number “1” should be somewhere on line 1 p1,1,1 ∨ p1,2,1 ∨ p1,3,1 ∨ p1,4,1 Number “1” should be at most once on line 1 ¬p1,1,1 ∨ ¬p1,2,1 ¬p1,1,1 ∨ ¬p1,3,1 ¬p1,1,1 ∨ ¬p1,4,1 ¬p1,2,1 ∨ ¬p1,3,1 ¬p1,2,1 ∨ ¬p1,4,1 ¬p1,3,1 ∨ ¬p1,4,1 4 octobre 2013 4 / 7
  • 17. SMT = SAT + expressiveness SAT solvers ¬ (p ⇒ q) ⇒ (¬p ⇒ q) ⇒ q Congruence closure (uninterpreted symbols + equality) a = b ∧ f(a) = f(b) ∨ (p(a) ∧ ¬p(b)) adding arithmetic a ≤ b ∧ b ≤ a + x ∧ x = 0 ∧ f(a) = f(b) ∨ (p(a) ∧ ¬p(b + x)) . . . Some examples : Barcelogic, CVC4 (SVC, CVC, CVC-lite, CVC3), MathSAT, OpenSMT, Yices, Z3 . . . The solver 4 octobre 2013 5 / 7
  • 18. SMT internals SMT formula SMT solver 4 octobre 2013 6 / 7
  • 19. SMT internals SMT formula SMT solver SAT solver 4 octobre 2013 6 / 7
  • 20. SMT internals SMT formula SMT solver Boolean Model SAT solver 4 octobre 2013 6 / 7
  • 21. SMT internals SMT formula SMT solver Boolean Model Theory reasoner SAT solver 4 octobre 2013 6 / 7
  • 22. SMT internals SMT formula SMT solver Boolean Model Conflict clause Theory reasoner SAT solver 4 octobre 2013 6 / 7
  • 23. SMT internals SMT formula SMT solver Quantifier-free SMT solver Boolean Model Conflict clause Theory reasoner SAT solver 4 octobre 2013 6 / 7
  • 24. SMT internals SMT formula SMT solver Quantifier-free SMT solver Boolean Model Conflict clause Theory reasoner SAT solver Model 4 octobre 2013 6 / 7
  • 25. SMT internals SMT formula SMT solver Quantifier-free SMT solver Boolean Model Conflict clause Theory reasoner SAT solver Instantiation module Instance Model 4 octobre 2013 6 / 7
  • 26. SMT internals SMT formula SMT solver Quantifier-free SMT solver Boolean Model Conflict clause Theory reasoner SAT solver Instantiation module Instance Model Model UNSAT (proof/core) 4 octobre 2013 6 / 7
  • 27. Conclusion Reasoning on large formulas with arithmetic, functions, equalities,. . . Applications planning placement bounded model-checking verification . . . The solver http://www.verit-solver.org BSD license 4 octobre 2013 7 / 7