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Using Problem Structure for Efficient Clause Learning

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Using Problem Structure for Efficient Clause Learning

Ashish Sabharwal, Paul Beame, Henry Kautz

University of Washington, Seattle

April 23, 2003

CNF encoding f

SAT solver

Input p2D

p : Instance

D : Domaingraph problem,

AI planning, model checking

f SAT

f SAT

p bad

p good

University of Washington

- Problem instances typically have structure
- Graphs, precedence relations, cause and effects
- Translation to CNF flattens this structure

- Best complete SAT solvers are
- DPLL based clause learners; branch and backtrack
- Critical: Variable order used for branching

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- Can we extract structure efficiently?
- In translation to CNF formula itself
- From CNF formula
- From higher level description

- How can we exploit this auxiliary information?
- Tweak SAT solver for each domain
- Tweak SAT solver to use general “guidance”

University of Washington

CNF encoding f

Branching sequence

SAT solver

Input p2D

f SAT

f SAT

Encode “structure”

as branching sequence

p bad

p good

University of Washington

- Exploiting structure in CNF formula
- [GMT’02] Dependent variables
- [OGMS’02]LSAT (blocked/redundant clauses)
- [B’01] Binary clauses
- [AM’00]Partition-based reasoning

- Exploiting domain knowledge
- [S’00] Model checking
- [KS’96]Planning (cause vars / effect vars)

University of Washington

- Structure can be efficiently retrieved from highlevel description (pebbling graph)
- Branching sequence as auxiliary information can be easily exploited

Given a pebbling graphG, can efficiently generate

a branching sequenceBG that dramatically improves

the performance of current best SAT solvers on fG.

University of Washington

Conjunction

of clauses

f = (x1ORx2OR:x9) AND (:x3ORx9)AND (:x1OR:x4OR:x5OR:x6)

University of Washington

DPLL(CNF formula f) {

Simplify(f);

If (conflict) return UNSAT;

If (all-vars-assigned) {return SAT assignment; exit}

Pick unassigned variable x;

Try DPLL(f |x=0), DPLL(f |x=1)

}

University of Washington

- DPLL: Change “if (conflict) return UNSAT”to “if (conflict) {learn conflict clause; return UNSAT}”

x2 = 1, x3 = 0, x6 = 0 ) conflict

“Learn” (:x2ORx3ORx6)

University of Washington

- B = (x1, x4, :x3, x1, :x8, :x2,:x4, x7, :x1, x2)
- DPLL: Change “Pick unassigned var x”to “Pick next literal xfrom B; delete it from B; if x already assigned, repeat”
- How “good” is B?
- Depends on backtracking process, learning scheme

Different from

“branching order”

University of Washington

Node E is pebbled if(e1ORe2) = 1

fG = Pebbling(G)

Source axioms:A, B, C are pebbled

Pebbling axioms:

A and B are pebbled)E is pebbled

…

Target axioms:

T is not pebbled

Target(s)

(t1ORt2)

T

(e1ORe2)

E

F

(f1)

A

B

C

(c1ORc2ORc3)

(a1ORa2)

(b1ORb2)

Sources

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- Can have
- Multiple targets
- Unbounded fanin
- Large clause labels

- Pebbling(G) is unsatisfiable
- Removing any clause from subgraph of each target makes it satisfiable

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(n1 n2)

m1

(t1 t2)

l1

(h1 h2)

(h1 h2)

(i1 i2)

e1

(i1 i2 i3 i4)

f1

(e1 e2)

(f1 f2)

(g1 g2)

(d1 d2 d3)

(g1 g2)

(a1 a2)

(b1 b2)

(c1 c2)

(d1 d2)

(a1 a2)

(c1 c2 c3)

b1

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- Practically useful
- precedence relations in tasks, fault propagation in circuits, restricted planning problems

- Theoretically interesting
- Used earlier for separating proof complexity classes
- “Easy” to analyze

- Hard for current best SAT solvers like zChaff
- Shown by our experiments

University of Washington

- Efficient : Q(|fG|)
- zChaff : One of the current best SAT solvers

Given a pebbling graphG, can efficiently generate

a branching sequenceBG such that zChaff(fG, BG) is

empirically exponentially faster than zChaff(fG).

University of Washington

- Input:
- Pebbling graphG

- Output:
- Branching sequenceBG, |BG| = Q(|fG|), that works well for 1UIP learning scheme and fast backtracking[fG : CNF encoding of pebbling(G)]

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- Compute node heights
- Foreach u2 {unit clause labeled nodes} bottom up
- Add u to G.sources
- GenSubseq(u)

- Foreach t2 {targets} bottom up
- GenSubseq(t)

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// trivial wrapper

- If (|v.preds| > 0)
- GenSubseq(v, |v.preds|)

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- u = v.preds[i] // by increasing height
- if i=1 // lowest pred
- GenSubseq(u) if unvisited non-source
- return

- Output u.labels// higher pred
- GenSubseq(u) if unvisitedHigh non-source
- GenSubseq(v, i-1)// recurse on i-1
- GenPattern(u, v, i-1)// repetitive pattern

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- Pure DPLL upto 60 variables
- DPLL +upto 60 variablesbranching seq
- Clause learningupto 4,000 variables(original zChaff)
- Clause learningupto 2,000,000 variables+ branching seq

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- Pure DPLL upto 35 variables
- DPLL +upto 50 variablesbranching seq
- Clause learningupto 350 variables(original zChaff)
- Clause learningupto 1,000,000 variables+ branching seq

University of Washington

- High level problem description is useful
- Domain knowledge can help SAT solvers

- Branching sequence
- One good way to encode structure

- Pebbling problems: Proof of concept
- Can efficiently generate good branching sequence
- Structure use improves performance dramatically

University of Washington

- Other domains?
- STRIPS planning problems (layered structure)
- Bounded model checking

- Variable ordering strategies from BDDs?
- Other ways of exploiting structure?
- branching “order”
- something to guide learning?
- Domain-based tweaking of SAT algorithms

University of Washington