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clpb.pl -- CLP(B): Constraint Logic Programming over Boolean Variables
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This library provides CLP(B), Constraint Logic Programming over Boolean variables. It can be used to model and solve combinatorial problems such as verification, allocation and covering tasks.

CLP(B) is an instance of the general CLP(_X_) scheme, extending logic programming with reasoning over specialised domains.

The implementation is based on reduced and ordered Binary Decision Diagrams (BDDs).

Usage examples of this library are available in a public git repository: github.com/triska/clpb

We recommend the following reference (PDF: metalevel.at/swiclpb.pdf) for citing this library in scientific publications:

  author    = "Markus Triska",
  title     = "The {Boolean} Constraint Solver of {SWI-Prolog}:
               System Description",
  booktitle = "FLOPS",
  series    = "LNCS",
  volume    = 9613,
  year      = 2016,
  pages     = "45--61"

and the following URL to link to its documentation:


Boolean expressions

A Boolean expression is one of:

variableunknown truth value
atomuniversally quantified variable
~ Exprlogical NOT
Expr + Exprlogical OR
Expr * Exprlogical AND
Expr # Exprexclusive OR
Var ^ Exprexistential quantification
Expr =:= Exprequality
Expr =\= Exprdisequality (same as #)
Expr =< Exprless or equal (implication)
Expr >= Exprgreater or equal
Expr < Exprless than
Expr > Exprgreater than
card(Is,Exprs)see below
+(Exprs)see below
*(Exprs)see below

where Expr again denotes a Boolean expression.

The Boolean expression card(Is,Exprs) is true iff the number of true expressions in the list Exprs is a member of the list Is of integers and integer ranges of the form From-To.

+(Exprs) and *(Exprs) denote, respectively, the disjunction and conjunction of all elements in the list Exprs of Boolean expressions.

Atoms denote parametric values that are universally quantified. All universal quantifiers appear implicitly in front of the entire expression. In residual goals, universally quantified variables always appear on the right-hand side of equations. Therefore, they can be used to express functional dependencies on input variables.

Interface predicates

The most frequently used CLP(B) predicates are:

True iff the Boolean expression Expr is satisfiable.
taut(+Expr, -T)
If Expr is a tautology with respect to the posted constraints, succeeds with T = 1. If Expr cannot be satisfied, succeeds with T = 0. Otherwise, it fails.
Assigns truth values to the variables Vs such that all constraints are satisfied.

The unification of a CLP(B) variable X with a term T is equivalent to posting the constraint sat(X=:=T).


Here is an example session with a few queries and their answers:

?- use_module(library(clpb)).

?- sat(X*Y).
X = Y, Y = 1.

?- sat(X * ~X).

?- taut(X * ~X, T).
T = 0,

?- sat(X^Y^(X+Y)).

?- sat(X*Y + X*Z), labeling([X,Y,Z]).
X = Z, Z = 1, Y = 0 ;
X = Y, Y = 1, Z = 0 ;
X = Y, Y = Z, Z = 1.

?- sat(X =< Y), sat(Y =< Z), taut(X =< Z, T).
T = 1,

?- sat(1#X#a#b).

The pending residual goals constrain remaining variables to Boolean expressions and are declaratively equivalent to the original query. The last example illustrates that when applicable, remaining variables are expressed as functions of universally quantified variables.

Obtaining BDDs

By default, CLP(B) residual goals appear in (approximately) algebraic normal form (ANF). This projection is often computationally expensive. You can set the Prolog flag clpb_residuals to the value bdd to see the BDD representation of all constraints. This results in faster projection to residual goals, and is also useful for learning more about BDDs. For example:

?- set_prolog_flag(clpb_residuals, bdd).

?- sat(X#Y).
node(3)- (v(X, 0)->node(2);node(1)),
node(1)- (v(Y, 1)->true;false),
node(2)- (v(Y, 1)->false;true).

Note that this representation cannot be pasted back on the toplevel, and its details are subject to change. Use copy_term/3 to obtain such answers as Prolog terms.

The variable order of the BDD is determined by the order in which the variables first appear in constraints. To obtain different orders, you can for example use:

?- sat(+[1,Y,X]), sat(X#Y).
node(3)- (v(Y, 0)->node(2);node(1)),
node(1)- (v(X, 1)->true;false),
node(2)- (v(X, 1)->false;true).

Enabling monotonic CLP(B)

In the default execution mode, CLP(B) constraints are not monotonic. This means that adding constraints can yield new solutions. For example:

?-          sat(X=:=1), X = 1+0.

?- X = 1+0, sat(X=:=1), X = 1+0.
X = 1+0.

This behaviour is highly problematic from a logical point of view, and it may render declarative debugging techniques inapplicable.

Set the flag clpb_monotonic to true to make CLP(B) monotonic. If this mode is enabled, then you must wrap CLP(B) variables with the functor v/1. For example:

?- set_prolog_flag(clpb_monotonic, true).

?- sat(v(X)=:=1#1).
X = 0.
- Markus Triska
Source sat(+Expr) is semidet
True iff Expr is a satisfiable Boolean expression.
Source taut(+Expr, -T) is semidet
Tautology check. Succeeds with T = 0 if the Boolean expression Expr cannot be satisfied, and with T = 1 if Expr is always true with respect to the current constraints. Fails otherwise.
Source labeling(+Vs) is multi
Enumerate concrete solutions. Assigns truth values to the Boolean variables Vs such that all stated constraints are satisfied.
Source sat_count(+Expr, -Count) is det
Count the number of admissible assignments. Count is the number of different assignments of truth values to the variables in the Boolean expression Expr, such that Expr is true and all posted constraints are satisfiable.

A common form of invocation is sat_count(+[1|Vs], Count): This counts the number of admissible assignments to Vs without imposing any further constraints.


?- sat(A =< B), Vs = [A,B], sat_count(+[1|Vs], Count).
Vs = [A, B],
Count = 3,

?- length(Vs, 120),
   sat_count(+Vs, CountOr),
   sat_count(*(Vs), CountAnd).
Vs = [...],
CountOr = 1329227995784915872903807060280344575,
CountAnd = 1.
Source random_labeling(+Seed, +Vs) is det
Select a single random solution. An admissible assignment of truth values to the Boolean variables in Vs is chosen in such a way that each admissible assignment is equally likely. Seed is an integer, used as the initial seed for the random number generator.
Source weighted_maximum(+Weights, +Vs, -Maximum) is multi
Enumerate weighted optima over admissible assignments. Maximize a linear objective function over Boolean variables Vs with integer coefficients Weights. This predicate assigns 0 and 1 to the variables in Vs such that all stated constraints are satisfied, and Maximum is the maximum of sum(Weight_i*V_i) over all admissible assignments. On backtracking, all admissible assignments that attain the optimum are generated.

This predicate can also be used to minimize a linear Boolean program, since negative integers can appear in Weights.


?- sat(A#B), weighted_maximum([1,2,1], [A,B,C], Maximum).
A = 0, B = 1, C = 1, Maximum = 3.