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# NAME
Algorithm::SAT::Backtracking - A simple Backtracking SAT solver written in pure Perl
# SYNOPSIS
use Algorithm::SAT::Backtracking;
my $solver = Algorithm::SAT::Backtracking->new;
my $variables = [ 'blue', 'green', 'yellow', 'pink', 'purple' ];
my $clauses = [
[ 'blue', 'green', '-yellow' ],
[ '-blue', '-green', 'yellow' ],
[ 'pink', 'purple', 'green', 'blue', '-yellow' ]
];
my $model = $solver->solve( $variables, $clauses );
$model = {
'green' => 1,
'yellow' => 1,
'blue' => 1
}
# DESCRIPTION
Algorithm::SAT::Backtracking is a pure Perl implementation of a simple SAT Backtracking solver.
In computer science, the Boolean Satisfiability Problem (sometimes called Propositional Satisfiability Problem and abbreviated as _SATISFIABILITY_ or _SAT_) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values **TRUE** or **FALSE** in such a way that the formula evaluates to **TRUE**.
If this is the case, the formula is called satisfiable. On the other hand, if no such assignment exists, the function expressed by the formula is identically **FALSE** for all possible variable assignments and the formula is unsatisfiable.
For example, the formula "a AND NOT b" is satisfiable because one can find the values a = **TRUE** and b = **FALSE**, which make (a AND NOT b) = TRUE. In contrast, "a AND NOT a" is unsatisfiable. More: [https://en.wikipedia.org/wiki/Boolean\_satisfiability\_problem](https://en.wikipedia.org/wiki/Boolean_satisfiability_problem) .
Look also at the test file for an example of usage.
[Algorithm::SAT::Expression](https://metacpan.org/pod/Algorithm::SAT::Expression) use this module to solve Boolean expressions.
# METHODS
## solve()
The input consists of a boolean expression in Conjunctive Normal Form.
This means it looks something like this:
`(blue OR green) AND (green OR NOT yellow)`
We encode this as an array of strings with a `-` in front for negation:
`[['blue', 'green'], ['green', '-yellow']]`
Hence, each row means an **AND**, while a list groups two or more **OR** clauses.
Returns 0 if the expression can't be solved with the given clauses, the model otherwise.
Have a look at [Algorithm::SAT::Expression](https://metacpan.org/pod/Algorithm::SAT::Expression) to see how to use it in a less painful way.
## resolve()
Uses the model to resolve some variable to its actual value, or undefined if not present.
my $model = { blue => 1, red => 0 };
my $a=$solver->resolve( "blue", $model );
#$a = 1
## satisfiable()
Determines whether a clause is satisfiable given a certain model.
my $model
= { pink => 1, purple => 0, green => 0, yellow => 1, red => 0 };
my $a=$solver->satisfiable( [ 'purple', '-pink' ], $model );
#$a = 0
## update()
Copies the model, then sets \`choice\` = \`value\` in the model, and returns it.
my $model
= { pink => 1, red => 0, purple => 0, green => 0, yellow => 1 };
my $new_model = $solver->update( $model, 'foobar', 1 );
# now $new_model->{foobar} is 1
# LICENSE
Copyright (C) mudler.
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.
# AUTHOR
mudler