AI-ExpertSystem-Advanced
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lib/AI/ExpertSystem/Advanced.pm view on Meta::CPAN
=over 4
=item *
Uses backward, forward and mixed algorithms.
=item *
Offers different views, so user can interact with the expert system via a
terminal or with a friendly user interface.
=item *
The knowledge database can be stored in any format such as YAML, XML or
databases. You just need to choose what driver to use and you are done.
=item *
Uses certainty factors.
=back
=head1 SYNOPSIS
An example of the mixed algorithm:
use AI::ExpertSystem::Advanced;
use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;
my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
{
filename => 'examples/knowledge_db_one.yaml'
});
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
initial_facts => ['I'],
verbose => 1);
$ai->mixed();
$ai->summary();
=cut
use Moose;
use AI::ExpertSystem::Advanced::KnowledgeDB::Base;
use AI::ExpertSystem::Advanced::Viewer::Base;
use AI::ExpertSystem::Advanced::Viewer::Factory;
use AI::ExpertSystem::Advanced::Dictionary;
use Time::HiRes qw(gettimeofday);
use YAML::Syck qw(Dump);
our $VERSION = '0.03';
=head1 Attributes
=over 4
=item B<initial_facts>
A list/set of initial facts the algorithms start using.
During the forward algorithm the task is to find a list of goals caused
by these initial facts (the only data we have in that moment).
Lets imagine your knowledge database is about symptoms and diseases. You need
to find what diseases are caused by the symptoms of a patient, these first
symptons are the initial facts.
Initial facts as also asked and inference facts can be negative or positive. By
default the initial facts are positive.
Keep in mind that the data contained in this array can be the IDs or the name
of the fact.
This array will be converted to L<initial_facts_dict>. And all the data (ids or
or names) will be made of only IDs.
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
initial_facts => ['I', ['F', '-'], ['G', '+']);
As you can see if you want to provide the sign of a fact, just I<encapsulate>
it in an array, the first item should be the fact and the second one the
sign.
=cut
has 'initial_facts' => (
is => 'rw',
isa => 'ArrayRef[Str]',
default => sub { return []; });
=item B<initial_facts_dict>
This dictionary (see L<AI::ExpertSystem::Advanced::Dictionary> has the sasme
data of L<initial_facts> but with the additional feature(s) of proviing
iterators and a quick way to find elements.
=cut
has 'initial_facts_dict' => (
is => 'ro',
isa => 'AI::ExpertSystem::Advanced::Dictionary');
=item B<goals_to_check>
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
goals_to_check => ['J']);
When doing the L<backward()> algorithm it's required to have at least one goal
(aka hypothesis).
This could be pretty similar to L<initial_facts>, with the difference that the
initial facts are used more with the causes of the rules and this one with
the goals (usually one in a well defined knowledge database).
The same rule of L<initial_facts> apply here, you can provide the sign of the
facts and you can provide the id or the name of them.
From our example of symptoms and diseases lets imagine we have the hypothesis
that a patient has flu, we don't know the symptoms it has, we want the
expert system to keep asking us for them to make sure that our hypothesis
is correct (or incorrect in case there's not enough information).
=cut
has 'goals_to_check' => (
is => 'rw',
isa => 'ArrayRef[Str]',
default => sub { return []; });
=item B<goals_to_check_dict>
Very similar to L<goals_to_check> (and indeed of L<initial_facts_dict>). We
want to make the job easier.
It will be a dictionary made of the data of L<goals_to_check>.
=cut
has 'goals_to_check_dict' => (
is => 'ro',
isa => 'AI::ExpertSystem::Advanced::Dictionary');
=item B<inference_facts>
Inference facts are basically the core of an expert system. These are facts
that are found and copied when a set of facts (initial, inference or asked)
match with the causes of a goal.
L<inference_facts> is a L<AI::ExpertSystem::Advanced::Dictionary>, it will
store the name of the fact, the rule that caused these facts to be copied to
this dictionary, the sign and the algorithm that triggered it.
=cut
has 'inference_facts' => (
is => 'ro',
isa => 'AI::ExpertSystem::Advanced::Dictionary');
=item B<knowledge_db>
The object reference of the knowledge database L<AI::ExpertSystem::Advanced> is
using.
=cut
has 'knowledge_db' => (
is => 'rw',
isa => 'AI::ExpertSystem::Advanced::KnowledgeDB::Base',
required => 1);
=item B<asked_facts>
During the L<backward()> algorithm there will be cases when there's no clarity
if a fact exists. In these cases the L<backward()> will be asking the user
(via automation or real questions) if a fact exists.
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 => $yaml_kdb,
initial_facts => ['I'],
verbose => 1);
By default this is turned off. If you want to know what happens behind the
scenes turn this on.
lib/AI/ExpertSystem/Advanced.pm view on Meta::CPAN
=over 4
=item B<+> or L<FACT_SIGN_POSITIVE>
In case user knows of it.
=item B<-> or L<FACT_SIGN_NEGATIVE>
In case user doesn't knows of it.
=item B<~> or L<FACT_SIGN_UNSURE>
In case user doesn't have any clue about the given fact.
=back
=cut
sub ask_about {
my ($self, $fact) = @_;
# The knowledge db has questions for this fact?
my $question = $self->{'knowledge_db'}->get_question($fact);
if (!defined $question) {
$question = "Do you have $fact?";
}
my @options = qw(Y N U);
my $answer = $self->{'viewer'}->ask($question, @options);
return $answer;
}
=head2 B<get_rule_by_goal($goal)>
Looks in the L<knowledge_db> for the rule that has the given goal. If a rule
is found its number is returned, otherwise undef.
=cut
sub get_rule_by_goal {
my ($self, $goal) = @_;
return $self->{'knowledge_db'}->find_rule_by_goal($goal);
}
=head2 B<forward()>
use AI::ExpertSystem::Advanced;
use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;
my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
{
filename => 'examples/knowledge_db_one.yaml'
});
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
initial_facts => ['F', 'J']);
$ai->forward();
$ai->summary();
The forward chaining algorithm is one of the main methods used in Expert
Systems. It starts with a set of variables (known as initial facts) and reads
the available rules.
It will be reading rule by rule and for each one it will compare its causes
with the initial, inference and asked facts. If all of these causes are in the
facts then the rule will be shoot and all of its goals will be copied/converted
to inference facts and will restart reading from the first rule.
=cut
sub forward {
my ($self) = @_;
confess "Can't do forward algorithm with no initial facts" unless
$self->{'initial_facts_dict'};
my ($more_rules, $current_rule) = (1, undef);
while($more_rules) {
$current_rule = $self->{'knowledge_db'}->get_next_rule($current_rule);
# No more rules?
if (!defined $current_rule) {
$self->{'viewer'}->debug("We are done with all the rules, bye")
if $self->{'verbose'};
$more_rules = 0;
last;
}
$self->{'viewer'}->debug("Checking rule: $current_rule") if
$self->{'verbose'};
if ($self->is_rule_shot($current_rule)) {
$self->{'viewer'}->debug("We already shot rule: $current_rule")
if $self->{'verbose'};
next;
}
$self->{'viewer'}->debug("Reading rule $current_rule")
if $self->{'verbose'};
$self->{'viewer'}->debug("More rules to check, checking...")
if $self->{'verbose'};
my $rule_causes = $self->get_causes_by_rule($current_rule);
# any of our rule facts match with our facts to check?
if ($self->compare_causes_with_facts($current_rule)) {
# shoot and start again
$self->shoot($current_rule, 'forward');
# Undef to start reading from the first rule.
$current_rule = undef;
next;
}
}
return 1;
}
=head2 B<backward()>
use AI::ExpertSystem::Advanced;
use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;
my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
{
filename => 'examples/knowledge_db_one.yaml'
});
my $ai = AI::ExpertSystem::Advanced->new(
viewer_class => 'terminal',
knowledge_db => $yaml_kdb,
goals_to_check => ['J']);
$ai->backward();
$ai->summary();
The backward algorithm starts with a set of I<assumed> goals (facts). It will
start reading goal by goal. For each goal it will check if it exists in the
initial, inference and asked facts (see L<is_goal_in_our_facts()>) for more
information).
=over 4
=item *
If the goal exist then it will be removed from the dictionary, it will also
verify if there are more visited rules to shoot.
If there are still more visited rules to shoot then it will check from what
rule the goal comes from, if it was copied from a rule then this data will
exist. With this information then it will see how many of the causes of this
given rule are still in the L<goals_to_check_dict>.
In case there are still causes of this rule in L<goals_to_check_dict> then the
amount of causes pending will be reduced by one. Otherwise (if the amount is
0) then the rule of this last removed goal will be shoot.
=item *
If the goal doesn't exist in the mentioned facts then the goal will be searched
in the goals of every rule.
In case it finds the rule that has the goal, this rule will be marked (added)
to the list of visited rules (L<visited_rules>) and also all of its causes
will be added to the top of the L<goals_to_check_dict> and it will start
reading again all the goals.
If there's the case where the goal doesn't exist as a goal in the rules then
it will ask the user (via L<ask_about()>) for the existence of it. If user is
not sure about it then the algorithm ends.
=back
=cut
sub backward {
my ($self) = @_;
my ($more_goals, $current_goal, $total_goals) = (
1,
0,
scalar(@{$self->{'goals_to_check'}}));
WAIT_FOR_MORE_GOALS: while($more_goals) {
READ_GOAL: while(my $goal = $self->{'goals_to_check_dict'}->iterate) {
if ($self->is_goal_in_our_facts($goal)) {
$self->{'viewer'}->debug("The goal $goal is in our facts")
if $self->{'debug'};
# Matches with any visiited rule?
my $rule_no = $self->{'goals_to_check_dict'}->get_value(
$goal, 'rule');
# Take out this goal so we don't end with an infinite loop
$self->{'viwer'}->debug("Removing $goal from goals to check")
if $self->{'debug'};
$self->{'goals_to_check_dict'}->remove($goal);
# Update the iterator
$self->{'goals_to_check_dict'}->populate_iterable_array();
# no more goals, what about rules?
lib/AI/ExpertSystem/Advanced.pm view on Meta::CPAN
$answer eq FACT_SIGN_POSITIVE or
$answer eq FACT_SIGN_NEGATIVE) {
$self->{'asked_facts'}->append($goal,
{
name => $goal,
sign => $answer,
algorithm => 'backward'
});
} else {
$self->{'viewer'}->debug(
"Don't know of $goal, nothing else to check"
);
return 0;
}
$self->{'goals_to_check_dict'}->populate_iterable_array();
$more_goals = 1;
next WAIT_FOR_MORE_GOALS;
}
}
}
}
return 1;
}
=head2 B<mixed()>
As its name says, it's a mix of L<forward()> and L<backward()> algorithms, it
requires to have at least one initial fact.
The first thing it does is to run the L<forward()> algorithm (hence the need of
at least one initial fact). If the algorithm fails then the mixed algorithm
also ends unsuccessfully.
Once the first I<run> of L<forward()> algorithm happens it starts looking for
any positive inference fact, if only one is found then this ends the algorithm
with the assumption it knows what's happening.
In case no positive inference fact is found then it will start reading the
rules and creating a list of intuitive facts.
For each rule it will get a I<certainty factor> of its causes versus the
initial, inference and asked facts. In case the certainity factor is greater or
equal than L<found_factor> then all of its goals will be copied to the
intuitive facts (eg, read it as: it assumes the goals have something to do with
our first initial facts).
Once all the rules are read then it verifies if there are intuitive facts, if
no facts are found then it ends with the intuition, otherwise it will run the
L<backward()> algorithm for each one of these facts (eg, each fact will be
converted to a goal). After each I<run> of the L<backward()> algorithm it will
verify for any positive inference fact, if just one is found then the algorithm
ends.
At the end (if there are still no positive inference facts) it will run the
L<forward()> algorithm and restart (by looking again for any positive inference
fact).
A good example to understand how this algorithm is useful is: imagine you are
a doctor and know some of the symptoms of a patient. Probably with the first
symptoms you have you can get to a positive conclusion (eg that a patient has
I<X> disease). However in case there's still no clue, then a set of questions
(done by the call of L<backward()>) of symptons related to the initial symptoms
will be asked to the user. For example, we know that that the patient has a
headache but that doesn't give us any positive answer, what if the patient has
flu or another disease? Then a set of these I<related> symptons will be asked
to the user.
=cut
sub mixed {
my ($self) = @_;
if (!$self->forward()) {
$self->{'viewer'}->print_error("The first execution of forward failed");
return 0;
}
use Data::Dumper;
while(1) {
# We are satisfied if only one inference fact is positive (eg, means we
# got to our result)
while(my $fact = $self->{'inference_facts'}->iterate) {
my $sign = $self->{'inference_facts'}->get_value($fact, 'sign');
if ($sign eq FACT_SIGN_POSITIVE) {
$self->{'viewer'}->debug(
"We are done, a positive fact was found"
);
return 1;
}
}
my $intuitive_facts = AI::ExpertSystem::Advanced::Dictionary->new(
stack => []);
my ($more_rules, $current_rule) = (1, undef);
while($more_rules) {
$current_rule = $self->{'knowledge_db'}->get_next_rule($current_rule);
# No more rules?
if (!defined $current_rule) {
$self->{'viewer'}->debug("We are done with all the rules, bye")
if $self->{'verbose'};
$more_rules = 0;
last;
}
# Wait, we already shot this rule?
if ($self->is_rule_shot($current_rule)) {
$self->{'viewer'}->debug("We already shot rule: $current_rule")
if $self->{'verbose'};
next;
}
my $factor = $self->get_causes_match_factor($current_rule);
if ($factor ge $self->{'found_factor'} && $factor lt 1.0) {
# Copy all of the goals (usually only one) of the current rule to
# the intuitive facts
my $goals = $self->get_goals_by_rule($current_rule);
while(my $goal = $goals->iterate_reverse) {
$intuitive_facts->append($goal,
{
name => $goal,
sign => $goals->get_value($goal, 'sign')
});
}
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