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Going back to the L<initial_facts> example of symptoms and diseases. Imagine
the algorithm is checking a rule, some of the facts of the rule make a match
with the ones of L<initial_facts> or L<inference_facts> but some wont, for
these I<unsure> facts the L<backward()> will ask the user if a symptom (a fact)
exists. All these asked facts will be stored here.

=cut
has 'asked_facts' => (
        is => 'ro',
        isa => 'AI::ExpertSystem::Advanced::Dictionary');

=item B<visited_rules>

Keeps a record of all the rules the algorithms have visited and also the number
of causes each rule has.

=cut
has 'visited_rules' => (
        is => 'ro',
        isa => 'AI::ExpertSystem::Advanced::Dictionary');

=item B<verbose>

    my $ai = AI::ExpertSystem::Advanced->new(
            viewer_class => 'terminal',
            knowledge_db => $yaml_kdb,
            initial_facts => ['I'],
            verbose => 1);

By default this is turned off. If you want to know what happens behind the
scenes turn this on.

Everything that needs to be debugged will be passed to the L<debug()> method
of your L<viewer>.

=cut
has 'verbose' => (
        is => 'rw',
        isa => 'Bool',
        default => 0);

=item B<viewer>

Is the object L<AI::ExpertSystem::Advanced> will be using for printing what is
happening and for interacting with the user (such as asking the
L<asked_facts>).

This is practical if you want to use a viewer object that is not provided by
L<AI::ExpertSystem::Advanced::Viewer::Factory>.

=cut
has 'viewer' => (
        is => 'rw',
        isa => 'AI::ExpertSystem::Advanced::Viewer::Base');

=item B<viewer_class>

Is the the class name of the L<viewer>.

You can decide to use the viewers L<AI::ExpertSystem::Advanced::Viewer::Factory>
offers, in this case you can pass the object or only the name of your favorite
viewer.

=cut
has 'viewer_class' => (
        is => 'rw',
        isa => 'Str',
        default => 'terminal');

=item B<found_factor>

In your knowledge database you can give different I<weights> to the facts of
each rule (eg to define what facts have more I<priority>). During the
L<mixed()> algorithm it will be checking what causes are found in the
L<inference_facts> and in the L<asked_facts> dictionaries, then the total
number of matches (or total number of certainity factors of each rule) will
be compared versus the value of this factor, if it's higher or equal then the
rule will be triggered.

You can read the documentation of the L<mixed()> algorithm to know the two
ways this factor can be used.

=cut
has 'found_factor' => (
        is => 'rw',
        isa => 'Num',
        default => '0.5');

=item B<shot_rules>

All the rules that are shot are stored here. This is a hash, the key of each
item is the rule id while its value is the precision time when the rule was
shot.

The precision time is useful for knowing when a rule was shot and based on that
you can know what steps it followed so you can compare (or reproduce) them.

=back

=cut
has 'shot_rules' => (
        is => 'ro',
        isa => 'HashRef[Str]');

=head1 Constants

=over 4

=item * B<FACT_SIGN_NEGATIVE>

Used when a fact is negative, aka, a fact doesn't happen.

=cut
use constant FACT_SIGN_NEGATIVE => '-';

=item * B<FACT_SIGN_POSITIVE>

Used for those facts that happen.

=cut