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Title: CSPs: Adding Structure to SAT


1
CSPs Adding Structure to SAT
  • George Katsirelos Fahiem Bacchus
  • University of Toronto

2
Introduction
  • Finite domain Constraint Satisfaction Problems
    (CSPs).
  • Formally equivalent to SAT
  • Important practical differences.
  • Different algorithmic techniques have been
    developed in the two areas.
  • Understanding these can help cross fertilize both
    fields.

3
BackgroundThe SAT and CSP Formalisms
4
Formalism
5
Formalism
  • SAT h V, Ci
  • V V1, V2, , Vn is a set of Boolean
    variables
  • C c1, c2, , ck a set of clauses.
  • CSP h V, D, C i
  • V V1, V2, , Vn is a set of multi-valued
    variables
  • D D1, D2, , Dn is a set of value domains,
    with Di being the domain of values for variable
    Vi
  • C C1, C2, , Ck is a set of constraints.
  • In both CSP and SAT the aim is to find an
    assignment of values for all of the variables
  • In SAT these values must satisfy the clauses
  • In CSPs these values must satisfy the
    constraints.

6
Constraints
  • A constraint C(X1,X2, , Xk) over the variables
    X1, , Xk is a Boolean function
  • It maps assignments to these variables to 0,1
    C(X1,X2, , Xk) DX1 ? DXk ? 0,1
  • If a tuple of assignments maps to 1, then these
    assignments satisfy the constraint, otherwise
    these assignments the falsify the constraint.

7
Extensionally vs Intensionally Represented
Constraints
  • We can specify the constraint with a table
  • C(X,Y,Z) with DX DY DZ 1, 2, 3

8
Extensionally vs Intensionally Represented
Constraints
  • Thus we can represent the constraint as a set of
    satisfying assignment tuples

9
Extensionally vs Intensionally Represented
Constraints
  • Or as a set of falsifying assignment tuples

10
Extensionally vs Intensionally Represented
Constraints
  • Extensional representations specify the
    constraint as an explicit list of satisfying
    assignments (or falsifying assignments).
  • Extensional representations were used in the 2005
    CSP solver competition. But are almost never used
    in practice.
  • The extensional representation becomes very
    large, growing exponentially with the number of
    variables the constraint is over.

11
Extensionally vs Intensionally Represented
Constraints
  • Constraint could also be represented
    intensionally as an algorithm for computing the
    Boolean function.

12
Extensionally vs Intensionally Represented
Constraints

X Y Z
13
Extensionally vs Intensionally Represented
Constraints
  • Intensional representations are typical in
    practice.
  • To specify a CSP problem in a CSP solver one
    supplies subroutines to implementing the
    constraints of the problem.
  • Commercial CSP solvers supply a large library of
    predefined common constraints.
  • You then simply specify the variables of the CSP,
    their domains, and the constraints that are over
    them.

14
Translating between SAT and CSPs
  • Further insight into the relation between SAT and
    CSPs is provided by looking at how we can
    translate between the formalisms.

15
SAT ? CSP
  • Translating in this direction is trivial
  • Each SAT variable becomes a CSP variable, with
    0,1 as its domain of values.
  • Each clause is equivalent to a Boolean function
    from the variables it is over
  • (x, y, -z) A function mapping (x0,y0,z1) to
    0, all other assignments of x,y,z to 1.

16
CSP ? SAT
  • The other direction requires two steps
  • Converting the multi-valued variables into a set
    of Boolean assignment variables.
  • Converting the constraints into clauses over the
    assignment variables.

17
CSP ? SATConverting the Multi-Valued Variables
  • Let X be a CSP variable with Dx d1, , dm
  • We create m Boolean assignment variables x1, x2,
    , xm these have the the interpretation
  • xi is true iff Xdi.

18
CSP ? SAT
  • The CSP variable X must have a value and it must
    have a unique value.
  • Hence the Boolean assignment variables x1, x2, ,
    xm associated with a particular CSP variable are
    mutually exclusive and exhaustive.
  • This is captured by adding the clauses
  • (x1, x2, , xm) X must have a value
  • (-xi, -xj) for all (i ? j) X has a unique value

19
CSP ? SAT Converting the Constraints into Clauses
  • Now we convert the constraints to clauses.
  • Each falsifying assignment tuple in the
    constraints extensional representation is
    equivalent to a clause.
  • So a constraint becomes a set of clauses, one for
    each falsifying assignment.

20
CSP ? SAT
  • Each falsifying tuple is a set of assignment
    variables that cannot be simultaneously true.
  • E.g.. (x1 y2 z1)
  • Pushing the negation in we get a clause(-x1 _
    -y2 _ -z1)

21
CSP ? SAT
  • There are various optimizations that can be
    applied to this basic translation.
  • Specific constraints admit more compact encodings.

22
Modeling with CSPs
23
Modeling with CSPs
  • CSPs offer
  • Multi valued variables more natural for modeling
    real problems.
  • Constraints over groups of variables that permit
    a more natural encoding of the constraints of the
    problem.
  • Industrial applications are much easier to
    formalize using CSPs, and the range of
    application of CSP technology in industry far
    exceeds that of SAT.

24
N-Queens
  • Place N queens on an NxN chess board so that no
    queen can attack any other queen.

25
N-Queens
  • Place N queens on an NxN chess board so that
    queen can attack any other queen.
  • N, Queen variables, one for each column

26
N-Queens
  • Place N queens on an NxN chess board so that
    queen can attack any other queen.
  • N values for each variable
  • The row we place that columns queen on.

27
N-Queens
  • Constraints
  • AllDiff(Q1, , QN) each Queen has a unique value
    (cant be in the same row)
  • Cij(Qi,Qj) Qi Qj ? i-j (for each i ? j)
  • cant be on same diagonal

28
Modeling with CSPs
  • A SAT encoding of N-Queens more complex to
    specify.
  • SAT encodings almost impossible to generate by
    hand.

29
Modeling with CSPs
  • Modeling using the richer language of CSPs,
    translate to SAT (automatically), solve using
    standard SAT solver.
  • Understanding the pros and cons of this approach
    gives us further insight into the algorithmic
    differences between CSP and SAT solvers.

30
Solving CSPs
31
Backtracking Search
  • SAT and CSP backtracking solvers differ in the
    three main parts of backtracking
  • Propagation as we descend the search tree
  • Learning as we ascend from failed subtrees
  • Heuristics for guiding the branching decisions

32
Translation to SAT
  • The clause learning in SAT solvers can be
    exploited.
  • The mutually exclusive and exhaustive clauses for
    the multi-valued variables are not fully
    exploited.
  • Branching heuristics insensitive to CSP
    structure.
  • Unit propagation weaker than propagation methods
    employed in CSP solvers.

33
Disadvantages(a) Clauses for Multi-valued
Variables
34
Disadvantages(a) Clauses for Multi-valued
Variables
  • (x1, x2, , xm) X must have a
    value
  • (-xi, -xj) for all (i ? j) X has a unique
    value
  • These clauses impose a useful structure on the
    assignment variables.

35
Disadvantages(a) Clauses for Multi-valued
Variables
  • In general, the disjunction of any subset of
    positive literals is equivalent to the
    conjunction of the complimentary set of negative
    literals. E.g., if m4
  • x1 _ x2 x3 x4
  • x3 -x1 -x2 -x4

36
Disadvantages(a) Clauses for Multi-valued
Variables
  • This structure could be exploited in various
    ways. For example,
  • Two negative assignment literals ? clause is
    redundant
  • (y1, y2, -x1, -x2, -z3) subsumed by (-x1,-x2)

37
Disadvantages(a) Clauses for Multi-valued
Variables
  • Negative assignment literal ? remove all positive
    literals from same variable.
  • (y1, y2, -y3, x1, -x2, -z3)
  • Resolve with (-y1, -y3) and (-y2, -y3) to obtain
    subsuming clause (-y3, -x2, -z3).

38
Disadvantages(a) Clauses for Multi-valued
Variables
  • Sets of clauses can be replaced by a single
    clause.
  • Dx Dy 1, 2, 3, 4(R, -x1, -y1) (R, -x1,
    -y2) (R, -x2, -y1) (R, -x2, -y2)
  • (R, -x1, -y1) (R, -x1, -y2) (R, -x2, -y1) (R,
    -x2, -y2)Equivalent to single clause(R, x3,
    x4, y3 , y4).

39
Disadvantages(a) Clauses for Multi-valued
Variables
  • (R, x3, x4, y3 , y4) (R, (-x2 -x1),
    (-y2 -y1))
  • Multiply this out and you get 8 clauses.

40
Disadvantages(b) Heuristics
41
Disadvantages (b) Heuristics
  • Under unit propagation contradictions arise when
    x is inferred in a context where x is already
    true
  • This causes some clause to be falsified (conflict
    clause).

42
Disadvantages (b) Heuristics
  • With multi-valued variables we always have
  • xi -x1 ? xi-1 xi1 ? -xm
  • Hence conflicts arise only from refuting all
    values from some CSP variables domain
  • -x1 ? ? -xm

43
Disadvantages (b) Heuristics
  • In CSP solvers the number of unrefuted values of
    a variable is always considered in the branching
    heuristic.
  • In a SAT solver we shouldnt choose to branch on
    xi without considering the status of other
    associated assignment variables.

Ansótegui1 et al 2003.
44
Disadvantages(c) Propagation
45
Disadvantages (c) Propagation
  • Unit Prop in a SAT solver on the clauses
    generated by a constraint is equivalent to
    Forward Checking in CSPs.
  • Forward Checking. Wait until all but one variable
    of the constraint is instantiated, and then prune
    incompatible values from the domain of the sole
    remaining uninstantiated variable.

46
Disadvantages (c) Propagation
  • (-x1 _ -y2 _ -z1)
  • (-x1 _ y3 _ z1)

47
Disadvantages (c) Propagation
  • Each clause contains one negated assignment
    literal from each CSP variable in the constraint.
  • To make the clause unit one has to make all but
    of these assignment variables true
  • Equivalent to assigning the corresponding CSP
    variable
  • x1 X1, y2 Y2

48
Disadvantages (c) Propagation
  • Then unit propagation will falsify all
    assignments to the remaining unassigned CSP
    variable that would violate the constraint
  • (-x1 _ y3 _ z1), (-x1 _ y3 _ z2)X1 Y3 ?
    Z ? 1 Z ? 2

49
Disadvantages (c) Propagation
  • However, in practice, FC does not perform
    particularly well.
  • A superior form of propagation is GAC.

50
GAC (Macworth Freuder 1977-79)
  • Given a constraint C(X1,X2, , Xk)
  • di 2 DXi is supported (in C) if there exists a
    set of assignmentsX1 d1, , Xi di ,, Xk
    dk that satisfies CC(X1d1, , Xk dk) 1.
  • This set is called a support for di.

51
GAC (Macworth Freuder 1977-79)
  • Supports for X 1 (x1)
  • Supports for x3If 3 is removed from the
    domain of Z, i.e., -z3 becomes true, x3 will
    loose its only support.

52
GAC (Macworth Freuder 1977-79)
  • The constraint C(X1,X2, , Xk) is said to be GAC
    if for all of its variables Xi every value in
    their domain is supported (in C).
  • We can make C(X1,X2, , Xk) GAC by removing all
    unsupported values from the domains of its
    variables.

53
GAC (Macworth Freuder 1977-79)
  • GAC propagation is the dynamic process of making
    all of the constraints GAC.
  • If d 2 DX is pruned from the domain of X while
    making C1 GAC.
  • Then the other constraints over X must have GAC
    reestablished.
  • This might prune values of other variables, and
    their constraints in turn must be made GAC once
    again.

54
GAC (Macworth Freuder 1977-79)
  • During search we make all constraints GAC at the
    root.
  • The assignment X1 means X ? 2, X ? 3,
  • Thus constraints over X have to have GAC
    reestablished by GAC propagation.
  • Reestablishing GAC at every node is called
    Maintaining GAC.

55
GAC Propagation in the SAT encoding
  • By using extra variables in the SAT encoding we
    can establish GAC with Unit Propagation.
  • But Unit Propagation on the standard encoding is
    less powerful than GAC.

Bessière et al. 2003
56
GAC Propagation in the SAT encoding
  • The power of GAC can be characterized using the
    notion of prime implicates.
  • If T is a set of clauses, then the clause c is a
    prime implicate if
  • c is non-tautological
  • T ² c
  • T ? c for any c that is a subset of c

57
GAC Propagation in the SAT encoding
  • Let TC be the set of clauses of the constraint C,
    along with the mutually exclusive and exhaustive
    clauses for each of the Cs variables.
  • Now we replace TC by the prime implicates of TC,
    PIc
  • Theorem Unit prop over PIc achieves precisely
    GAC propagation on the constraint C.

58
GAC Propagation in the SAT encoding
  • Note that GAC is local to the constraint.
    Communication between constraints occurs only
    through unit implicants (pruned values).
  • So GAC is complete local inference for units.

59
GAC Propagation in the SAT encoding
  • Achieving GAC over a generic constraint C(X1,X2,
    , Xk) requires time exponential in K.
  • However, there is however a huge body of
    knowledge in the CSP literature on how to achieve
    GAC on particular constraints in time polynomial
    in K.
  • These methods (called propagators) exploit the
    special structure of the constraint.

60
Translation to SAT Summary
  • The fundamental problems
  • is the size of the encoding
  • The vast body of knowledge about propagators for
    GAC cannot be exploited.
  • Exploiting propagators GAC has much in common
    with exploiting specialized theories in SMT.

61
CSP Solvers
62
Using a CSP solver
  • Constraints can be represented intensionally and
    propagators can be exploited.
  • The multi-valued variable structure, and
    information about the constraints can be
    exploited for branching decisions.
  • Learning in CSP solvers is much weaker than
    clause learning in Sat solvers, and it doesnt
    integrate well with GAC propagation.

63
Learning
  • We can improving learning in CSPs and achieve a
    better integration with GAC.
  • We can also integrate GAC propagators with
    learning using ideas that are essentially
    identical to those used in SMT
  • These ideas were developed independently.

64
Learning
  • In CSPs learning from failed subtrees has a long
    tradition. Learning is typically called nogood
    recording.

65
NoGoods
  • A NoGood in CSPs is a set of assignments that
    cannot be extended to a solution.
  • (X3, Y2, Z1)
  • Translating this to SAT we get -(x3 y2 z1)
    (-x3, -y2, -z1)
  • ? Nogoods are negative clauses
  • (clauses containing only negative literals).

66
Negative Resolution
  • Restricting learning to NoGoods (negative
    clauses) restricts the solvers resolution
    power to Negative Resolution.
  • Negative resolution every resolution step
    involves a clause negative clause.
  • Negative resolution not as powerful as general
    resolution
  • CSP solvers sometimes suffer a super-polynomial
    slowdown over SAT solvers running on the SAT
    encoding.

Mitchell 2003Katsirelos PhD thesis
67
Negative Resolution
  • As a result of this restriction to learning
    negative clauses learning is hardly ever used CSP
    solvers in practice.
  • Learning negative clauses is also produces
    particularly ineffective clauses from GAC.

68
Integrating SAT style Clause Learning
  • GAC prunes domain values. It forces negated
    assignment literals.
  • Like SMT all we need to do is to label those
    literals with clauses.

69
Clause Learning in CSP solvers
X 1 ? CHOICEX ? 2 ? (X ? 1, X ? 2) (variable
can only have one value)Y ? 1 ? (Y ? 1, X2)
(Non-negative clause reason from GAC)Z ? 1 ? (Z
? 1, X2) A 2 ? CHOICEA ? 1 ? A ? 3 ? X ?
1 ? (X ? 1, A ? 2) (conflict clause
from constraint over X, and A)
70
Clause Learning in CSP solvers
  • We can resolve backwards from a conflict along
    the implication trail from a conflict to learn
    various types of new clauses
  • I.e., we can apply standard SAT clause learning
    techniques.

71
Computing Clauses from GAC
  • With Unit Prop each literal is implied by a
    specific clause that became unit, so the clause
    for labeling an implied literal is obvious.
  • With GAC a value is pruned (an assignment
    variable is made false) as the result of many
    different clauses of the constraint.
  • In particular, a value is pruned by GAC when it
    looses all of its supports.
  • Each support (which is a tuple of assignments to
    the variables of the Constraint) is lost when one
    of its assignments is made false.

72
Computing Clauses from GAC
  • Supports for X 1
  • E.g., (X1 Y1 Z1). This support can be
    lost if 1 is pruned from the domain of Y or from
    Z. (Y?1, or Z?1)

73
Computing Clauses from GAC
  • If GAC on this constraint prunes X1, a reason
    for this pruning is a set of currently true
    non-assignments that hits all of X1s supports.

74
Computing Clauses from GAC
  • Supports for X 1
  • E.g.,Y?1, Z?2, Z?3 covers all of X1s supports
    in this constraint
  • Y?1Æ Z?2 Æ Z?3!X?1 (Y1, Z2, Z3, X?1)

75
Computing Clauses from GAC
  • We put this implication on the trailX?1 ? (X?1,
    Y1, Z2, Z3)
  • Note that
  • this is a non-negative clause
  • We can compute this clause on the fly from an
    extensional representation of the constraint.
  • There is no need to precompute and store all such
    possible pruning clauses.

76
Computing clauses from Intensional Constraints
  • How do we obtain clausal reasons from GAC
    propagators?
  • We can no longer find a hitting set for the
    supports of the value, there is no explicit
    representation of these supports.

77
Example All Different
  • AllDiff(X1, , Xn) is satisfied only by tuples of
    assignments to the Xi that are all different,
    i.e.,
  • i?j ! Xi?Xj

78
All Different
  • A way of enforcing GAC on AllDiff in poly-time
    was the probably the first propagator developed
    in the CSP literature. Regin 1994.
  • The method utilizes maximum matchings in
    bipartite graphs.
  • Since then dozens of propagators have been
    developed.

79
The power of propagators
  • DPLL must take exponential time on the pigeon
    hole problem PHP this problem is hard for
    general resolution.
  • PHP can be encoded as a single AllDiff(P1, ,
    Pn) each with domain of values 1, , n-1.
  • This constraint has no satisfying tuples so every
    value will be pruned by GAC.
  • A CSP solver can solve this problem in polynomial
    time. GAC propagation at the root, no search.

80
Clausal Reasons from AllDiff
  • How do we obtain a clausal reason for a value
    pruned by AllDiff?
  • For Alldiff a value is pruned from a variable
    domain only when that value is consumed by some
    other variables. That is, the value must be used
    by some other variable.

81
Clausal Reasons from AllDiff
  • DX 1, 2, 3,4, DY 1, 2, 3,4, DZ 1, 2,
    3, DW 1, 4, 5

DX 1, 2, 3,4, DY 1, 2, 3,4, DZ 1, 2,
3, DW 1, 4, 5
Prune 4 from domain of X and Y
Initially all values are supported
82
Clausal Reasons from AllDiff
DX 1, 2, 3,4, DY 1, 2, 3,4, DZ 1, 2,
3, DW 1, 4, 5
DX 1, 2, 3,4, DY 1, 2, 3,4, DZ 1, 2,
3, DW 1, 4, 5
1, 2, 3 are consumed by X, Y, Z. (Hall interval)
W cannot be assigned 1 since that value is
consumed.
83
Clausal Reasons from AllDiff
DX 1, 2, 3,4, DY 1, 2, 3,4, DZ 1, 2,
3, DW 1, 4, 5
  • X?4 Æ Y?4 ! W?1
  • Clause reason for W?1
  • (W?1,X4,Y4)

W cannot be assigned 1 since that value is
consumed.
84
Clausal Reasons from All Diff
  • If allDiff ? X?d the reason is
  • First find the set of variables that still have d
    in their domain.
  • These variables must be consuming d.
  • And the reason is the set of pruned values in
    their domains.

85
Computing clauses from Intensional Constraints
  • Katsirelos has found ways to compute clausal
    reasons for a variety of known propagators.
  • He has implemented this in a CSP solver called
    EFC, which contains
  • a number of built in intensional constraints
  • GAC propagators for them.
  • Clausal reasons supplied by these propagators.

86
Computing clauses from Intensional Constraints
  • This solver can be thought of as a multi-valued
    variable SMT solver, where T includes a set of
    constraints known in the CSP literature.
  • Like SMT solvers it can display very impressive
    performance.

87
Solving CSP via CSP solvers
  • More work still needs to be done
  • There are many more propagators that we dont yet
    know how to get clausal reasons from.
  • Heuristics remain poorly understood, and still
    need improvement.

88
Some Empirical Results
89
Logistics (AI Planning)
  • From Katsirelos Bacchus, Generalized NoGoods
    in CSPs

90
Social Golfer
91
Other ideas from CSP solvers for SAT/SMT
  • CSP solvers use propagation Queues
  • Sequences the propagators, as some propagators
    are more expensive.
  • Flexible ways of specifying the level of
    propagation for each constraint.
  • Only forward checking is preformed on some, GAC
    on others, we delay GAC until all but 3 variables
    of the constraint are instantiated etc.

92
Other ideas from CSP solvers for SAT/SMT
  • Bounds propagation.
  • Order the domain values and instead of pruning
    all unsupported values, we maintain upper and
    lower bounds on the possible domain values.
  • Possible to do more efficiently than GAC in many
    cases.
  • This would correspond to generating only a subset
    of the implied literals.
  • Once we get to a solution all literals are set
    anyway.

93
Other ideas from CSP solvers for SAT/SMT
  • Multi-valued variables are very useful. But not
    fully exploited in SAT solvers.
  • Huge body of known constraints and algorithms for
    propagating them.

94
Conclusions
  • CSPs add structure to SAT.
  • This structure can be exploited to make modeling
    easier.
  • Can be used to identify groups of clauses over
    which
  • higher levels of propagation can be profitable
  • Specialized non-resolution based algorithms can
    be exploited.
  • Connecting these extra kinds of reasoning with
    clause learning adds considerable extra power.
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