1234567890 from propositional sat to smt hossein m. sheini and karem a. sakallah sat 2006 august 13,...
TRANSCRIPT
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1234567890
From Propositional SAT to SMTHossein M. Sheini and Karem A. Sakallah
SAT 2006
August 13, 2006
Seattle
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Propositional Satisfiability
• DPLL: systematic backtracking search• Branch
• [Pure literal rule]
• Propagate• Unit propagation Boolean Constraint Propagation (BCP)
• Backtrack
• Modern implementations of DPLL• Conflict analysis
• Clause learning
• Non-chronological backtracking
• Efficient propagation• Two watched literals per clause
• Adaptive branching• VSIDS
• Restarts
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Quantifier-Free First-Order Logic
Boolean combination of atoms from background theories
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Decidable Quantifier-Free FOL Theories
• Empty• QF_UF: uninterpreted functions with equality (aka EUF)
• Linear Real Arithmetic• QF_LRA• QF_RDL: real difference logic• UTVPI
• Linear Integer Arithmetic• QF_LIA• QF_IDL: integer difference logic
• Data Structures• QF_A: arrays• QF_BV: bit vectors• Lists, etc.
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QF_UF Atoms
term ::= const | var | func-symbol(term, ,term)
atom ::= propositional-var | term = term
literal::= atom | atom
f a,b a
f f a,b ,b f a,b
f g x,y ,z ,h f a,b y
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Linear Real Arithmetic Atoms
R1 1 n n i ia x a x ~ b a ,x ,b ,~ , ,QF_LRA:
Rx y d x,y ,dQF_RDL:
R 0 1ax by c x,y ,c ,a,b ,UTVPI:
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Linear Integer Arithmetic Atoms
Z1 1 n n i ia x a x ~ b a ,x ,b ,~ , ,QF_LIA:
Zx y d x,y ,dQF_IDL:
Z 0 1ax by c x,y ,c ,a,b ,IUTVPI:
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Chronology of SAT for Quantifier-Free FOL
• Late 70s to mid 80s• Congruence closure algorithms• Combination strategies for disjoint theories
• Mid 80s to mid 90s• Not much!
• Mid to late 90s• Initial attempts at improved propositional reasoning
• Early 21st Century• Significant activity across many fields
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Reasoning About Equality
• To prove validity of EUF formula • Construct disjunctive normal form (DNF) of ¬• Prove unsatisfiability of each conjunct of ¬ using congruence closure
[Shostak 78][NelsonOppen 80]
Logic of equality with uninterpreted functions and predicates (EUF)
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Congruence Closure Prove f a,b a f f a,b ,b a
f a,b a f f a,b ,b a
a b
f
f
is unsatisfiable is valid
[NelsonOppen 80]
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Reasoning About Integer Arithmetic
• To prove validity of QF Presburger formula • Construct disjunctive normal form (DNF) of ¬• Prove unsatisfiability of each conjunct of ¬ using integer linear
programming (ILP)
Quantifier-Free Presburger Arithmetic
[Shostak 79]
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QF Presburger Arithmetic Example 3 2 1x y x x y
3 2 1x y x x y
Negate
Invalid!
3 1 1 1 1 1x y x x x y y x
“Normalize”
3 1 1 1 1
3 1 1 1 1
x y x x x y
x y x x y x
Convert to DNF
Solve ILPs
1 0x ,y[Shostak 79]
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Deciding Combinations of Theories
• Eliminate UFs and UPs using “Ackermann’s reduction” (adding consistency “axioms”) to get a pure equality formula
• Convert complement to DNF• Solve each conjunct as an integer linear program (ILP)• Formula explodes
[Shostak 79]
Quantifier-Free Presburger Arithmetic + EUF
Add all functional consistency axioms(aka substitutivity axioms of equality)
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QF Presburger Arithmetic + EUF Example
0 1 4 3 2 1 0h z z g y z f g y f z h
1 1 3 2 3 2
0 1 4 3 2 1 0
z h z h g y z f g y f z
h z z g y z f g y f z h
Add functional consistency axioms
1 2 3 4 5
1 3 4 5 2
1 3 2
0 1 4 0
z x x x z x x
x z x z x x x
Eliminate function symbols
[Shostak 79]
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Alternatively …
• Add all functional consistency axioms• Eliminate UFs and UPs using “Ackermann’s reduction”
(adding consistency “axioms”) to get a pure equality formula• Convert complement to DNF• Solve each conjunct as an integer linear program (ILP)• Formula explodes
• Add functional consistency axioms as needed• Ignore functional consistency and solve as before• Check functional consistency for symbols with different
values; if violated, add axiom and repeat• Has the flavor of “learning” on demand• ILPs can be solved incrementally
[Shostak 79]
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Deciding Combinations of Theories
• Convert negation to DNF• Add variables to purify different theory conjuncts• Solve separately• Propagate equalities• Split in case no equalities can be inferred• Supported theories:
• Real numbers under + and leq: Simplex• Arrays under store and select• List structures with car, cdr, cons, and atom: congruence
closure• Equality with UF: congruence closure
[NelsonOppen 79]
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Example of Nelson-Oppen Procedure
car cons 0 0x y y x ,x P h x h y P
ListsEUFLRA
2 1 5
1 5
2 3 4 3
5 4
true car cons
false
0
x y P g g g ,x
y x g P g
g g g g h x
g g h y
[NelsonOppen 79]
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Example of Nelson-Oppen Procedure
1 5car consg g ,x
5 0g
1 5g g
5 FP g
1 0g 1y x g
y x x y
x y
h x h y
3g h x 4g h y
2 3 4g g g
2 TP g
3 4g g
2 0g
2 5g g
[NelsonOppen 79]
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Deciding Combinations of Theories
• Generalizes Nelson-Oppen method by eliminating the need for extra variables
• Congruence closure is extended to handle different theories as long as they have “canonizers” and solvers
• Found to be “buggy” and not as general as N-O
[Shostak 84]
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Disadvantages of “Old” Combination Methods
• Need to convert to DNF• Inefficient handling of Boolean structure• Can be viewed as lazy integration with an open
feedback loop between a propositional enumerator and the theory solvers
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Disadvantages of “Old” Combination Methods
SAT
No
SMT Instance
DNFConverter
DNF Instance
Yes
MoreConjuncts?
Conjunction
of
Theory Atoms
Theory Solvers
UNSAT
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Improved Propositional Reasoning
• Convert SMT instance to equi-satisfiable propositional form and apply Boolean reasoning techniques (BDDs or SAT)• Small-domain encoding• Per-constraint encoding
• Add more sophisticated Boolean reasoning, but keep background theories intact• Re-write rules and better Boolean splits• Add full-fledged SAT or BDD solvers to handle the Boolean skeleton
• Very Lazy: theory solver returns a conflict clause to SAT solver
• Lazy: theory solver invoked incrementally but does not propagate
• Eager: theory solver propagates and learns etc.
• Variants
• Layered
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Reasoning About Equality: Revisited
MoreConjuncts?
CongruenceClosure
SAT
No
YesUNSAT
EUF Formula
DNFConverter
DNF of = and ≠ Conjuncts
Conjunction of= and ≠
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Reasoning About Equality: Revisited
SAT/BDDSolver
SATUNSAT
EUF Formula
Reduction
E Formula
Range Analysis&
Boolean Encoding
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Bryant’s “ite” Reduction
1 1
2 1 2
3 1 1 1 2 3
4 3 1 3 2 3 1 3 4
4 3 3 2 1
F f
F x y,f ,f
F F x,f , F y ,f ,f
F F x,f , F y ,f , F F ,f ,f
F F F F y F
ite
ite ite
ite ite ite
★
★
★ ★ ★
★ ★ ★ ★ ★
★ ★ ★ ★ ★
Reasoning About Equality: Revisited F F F x F F x F F x F y y F x
CongruenceClosure
x y
F1
F4
F3
F2
Ackermann’sReduction
1 2
1 1 3
3 1 4
1 2 3
3 2 4
1 3 3 4
4 3 3 2 1
x y f f
x f f f
x f f f
y f f f
y f f f
f f f f
f f f f y f
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Pros/Cons of SMT-to-SAT Conversion
Loss of theory semantics (e.g., arithmetic)
Black-box use of modern SAT solversLeveraging of performance/capacity improvementsin SAT solvers
Increase of instance sizes
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Combine SAT and Theory Solvers
SAT
UNSAT
SMT Instance
SAT Solver
PropositionalAbstraction
SAT Instance
SAT
Theory Atoms
Theory Solvers
AbstractionRefinement
UNSAT
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Propositional Abstraction
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Spectrum of Integration
• Very lazily: theory solver does not feedback any info to SAT solver; no refinement
• Lazily: theory solver returns a small explanation of infeasibility to SAT solver• Eagerly: theory solver participates in value propagation (implications) and in
conflict analysis• Very eagerly: direct encoding of all theory semantics in propositional
formula; no abstraction
How aggressively is refinement done?
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Very Lazy Integration
Boolean Solver
1A 1B
2A 2B
3A 3B
4A 4B
61B
71B
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Very Lazy Integration
Integer Solver
1
2
3
4
61
71
5
6
0
12
1
2
B u w
B v w
B z
B u v
B x z
B y z
5
6
0
12
1
2
u w
v w
z
u v
x z
y z
UNSAT
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Spectrum of Integration
• Very lazily: theory solver does not feedback any info to SAT solver; no refinement
• Lazily: theory solver returns a small explanation of infeasibility to SAT solver• Eagerly: theory solver participates in value propagation (implications) and in
conflict analysis• Very eagerly: direct encoding of all theory semantics in propositional
formula; no abstraction
How aggressively is refinement done?
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Lazy Integration
1A 1B
2A 2B
3A 3B
4A 4B
61B
71B
Boolean Solver
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Lazy Integration
11u v 0 1
5u w 1B
6v w 2B
12u v
1
2
3
4
61
71
5
6
0
12
1
2
B u w
B v w
B z
B u v
B x z
B y z
Integer Solver
3B 0z
Create conflict clause
and return to Boolean solver
1 2 4B B B 61B
71B
4B
1B
2B
4B
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Spectrum of Integration
• Very lazily: theory solver does not feedback any info to SAT solver; no refinement
• Lazily: theory solver returns a small explanation of infeasibility to SAT solver• Eagerly: theory solver participates in value propagation (implications) and in
conflict analysis• Very eagerly: direct encoding of all theory semantics in propositional
formula; no abstraction
How aggressively is refinement done?
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Eager Integration: Incremental Propagation
1A 1B
2A 2B
3A 3B
4A 4B
11u v
0 1
5u w
6v w
12u v
0z
1B
2B
4B
1 2 4B B B
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Eager Integration: Incremental Propagation
1A 1B
2A 2B
3A 4B
11u v
5u w
6v w
3B
0z
4A
61B
71B
1x z
2y z
1x
2y
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Integration Trade-offs
• Must balance cost of generating new “facts” against utility of such facts in pruning the search space• E.g., a priori generation of transitivity constraints for all
possible equalities is overkill• Suggests an “on-demand” learning strategy analogous to
conflict analysis in modern SAT solvers
• On-demand learning requires incremental backtrackable theory solvers that maintain state
• Cost of propagation for various theories and sub-theories:• EUF: congruence closure is O(n log n) • Difference constraints: negative cycle detection is O(nm)• UTVPI: transitive closure is O(n3)• Real arithmetic: incremental Simplex
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Offline Integration of LRA Solver
1A 1B
2A 2B
3A 4B
11u v
5u w
6v w
3B
0z
4A
61B
71B
1x z
2y z
1x
2y
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Offline Integration of LRA Solver
1A 1B
2A 2B
3A 4B
11u v
5u w
6v w
3B
0z
4A
61B
71B
1x z
2y z
1x
2y
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Offline Integration of LRA Solver
5
6
0
1
2
11
1
2
4 4 0
u w
v w
z
x z
y z
u
u v x
v
y
x
y
11
1
2
4 4 0
u v
x
y
u v x y
5
6
0
1
2
1
2
11
4 4 0
u
u w
v w
z
x z
y z
x
v
u v y
y
x
11
1
4 4
5
0
2
0
6
1
2
u v
x
u w
v w
z
x z
y z
y
u v x y
11
1
2
4 4
5
0
0
6
1
2
u v
x
u
y
u v x y
w
v w
z
x z
y z
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Learning Strategies
• CNF clauses: disjunctions of existing atoms (in terms of their indicator variables)
• Introduction of new theory atoms: cutting planes
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Introduction of New Atoms
1A 1B
2A 2B11u v
5u w
6v w
4 4 0u v x y
2x y
8BNEW
1 2 8B B B 1 2 8B B B
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Offline Integration of LRA Solver
1A 1B
2A 2B
3A 4B
11u v
5u w
6v w
3B
0z
4A
61B 1x z
1x
8B 2x y
1y
71B 72B 73B
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DPLL(T) Framework
• Declarative “calculus” for tight integration of a solver for theory T within a propositional DPLL solver
• Inspired by CLP(X)
• Defines SolverT as an abstract data type with the following methods:• Initialize(L: Literal set)• SetTrue(l: L-literal): L-literal set• IsTrue?(l: L-literal): Boolean• Backtrack(n: Natural)• Explanation(l: L-literal): L-literal set
[Tinelli 02][Nieuwenhuis-Oliveras 03]
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GETFOL1993
KSAT1996
MATH-SAT2001
MATH-SAT2002
MATH-SAT2005
LPSAT1999
CVC Lite2004
Nelson-Oppen1979
Shostak1984
SVC1996
CVC2002/2004
Verifun2003
Simplify1998?
ICS2001
ICS+Chaff2002
Simplics2005
DPLL(T)2002
Sammy2005
BarcelogicTools2005
Yices2006
Ario2005
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Nelson-Oppen1979
MATH-SAT2001
KSAT1996
GETFOL1993
MATH-SAT2002
MATH-SAT2005
LPSAT1999
SVC1996
CVC2002/2004
CVC Lite2004
Shostak1984
Simplify1998?
Verifun2003
ICS2001
Simplics2005
ICS+Chaff2002
DPLL(T)2002
Sammy2005
BarcelogicTools2005
Yices2006
Ario2005
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2005 Competition Results: QF_UF
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2005 Competition Results: QF_RDL
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2005 Competition Results: QF_IDL
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2005 Competition Results: QF_UFIDL
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2005 Competition Results: QF_LRA
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2005 Competition Results: QF_LIA
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2005 Competition Results: QF_AUFLIA
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Conclusions
• SAT does it again!• Modern SAT technology critical enabler of SMT solvers
• Clear winner: tight integration of SAT and Theory solvers• Incremental propagation• Incremental conflict analysis and learning• Careful tuning
• SMT is bringing different communities together (SAT, CP, AI, OR)
• Competition is good