Author image Pablo Fischer
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AI::ExpertSystem::Advanced - Expert System with backward, forward and mixed algorithms


Inspired in AI::ExpertSystem::Simple but with additional features:

  • Uses backward, forward and mixed algorithms.

  • Offers different views, so user can interact with the expert system via a terminal or with a friendly user interface.

  • 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.

  • Uses certainty factors.


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);



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 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 encapsulate it in an array, the first item should be the fact and the second one the sign.


This dictionary (see AI::ExpertSystem::Advanced::Dictionary has the sasme data of initial_facts but with the additional feature(s) of proviing iterators and a quick way to find elements.

    my $ai = AI::ExpertSystem::Advanced->new(
            viewer_class => 'terminal',
            knowledge_db => $yaml_kdb,
            goals_to_check => ['J']);

When doing the backward() algorithm it's required to have at least one goal (aka hypothesis).

This could be pretty similar to 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 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).


Very similar to goals_to_check (and indeed of initial_facts_dict). We want to make the job easier.

It will be a dictionary made of the data of goals_to_check.


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.

inference_facts is a 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.


The object reference of the knowledge database AI::ExpertSystem::Advanced is using.


During the backward() algorithm there will be cases when there's no clarity if a fact exists. In these cases the backward() will be asking the user (via automation or real questions) if a fact exists.

Going back to the 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 initial_facts or inference_facts but some wont, for these unsure facts the backward() will ask the user if a symptom (a fact) exists. All these asked facts will be stored here.


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

    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 debug() method of your viewer.


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

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


Is the the class name of the viewer.

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


In your knowledge database you can give different weights to the facts of each rule (eg to define what facts have more priority). During the mixed() algorithm it will be checking what causes are found in the inference_facts and in the 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 mixed() algorithm to know the two ways this factor can be used.


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.



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


    Used for those facts that happen.


    Used when there's no straight answer of a fact, eg, we don't know if an answer will change the result.


shoot($rule, $algorithm)

Shoots the given rule. It will do the following verifications:

  • Each of the facts (causes) will be compared against the initial_facts_dict, inference_facts and asked_facts (in this order).

  • If any initial, inference or asked fact matches with a cause but it's negative then all of its goals (usually only one by rule) will be copied to the inference_facts with a negative sign, otherwise a positive sign will be used.

  • Will add the rule to the shot_rules hash.


Verifies if the given $rule has been shot.


Will ask the knowledge_db for the goals of the given $rule.

A AI::ExpertSystem::Advanced::Dictionary will be returned.


Will ask the knowledge_db for the causes of the given $rule.

A AI::ExpertSystem::Advanced::Dictionary will be returned.

is_fact_negative($dict_name, $fact)

Will check if the given $fact of the given dictionary ($dict_name) is negative.

copy_to_inference_facts($facts, $sign, $algorithm, $rule)

Copies the given $facts (a dictionary, usually goal(s) of a rule) to the inference_facts dictionary. All the given goals will be copied with the given $sign.

Additionally it will add the given $algorithm and $rule to the inference facts. So later we can know how we got to a certain inference fact.


Compares the causes of the given $rule with:

  • Initial facts

  • Inference facts

  • Asked facts

It will be couting the matches of all of the above dictionaries, so for example if we have four causes, two make match with initial facts, other with inference and the remaining one with the asked facts, then it will evaluate to true since we have a match of the four causes.


Similar to compare_causes_with_facts() but with the difference that it will count the "match factor" of each matched cause and return the total of this weight.

The match factor is used by the mixed() algorithm and is useful to know if a certain rule should be shoot or not even if not all of the causes exist in our facts.

The match factor is calculated in two ways:

  • Will do a sum of the weight for each matched cause. Please note that if only one cause of a rule has a specified weight then the remaining causes will default to the total weight minus 1 and then divided with the total number of causes (matched or not) that don't have a weight.

  • If no weight is found with all the causes of the given rule, then the total number of matches will be divided by the total number of causes.


Checks if the given $goal is in:

  1. The initial facts

  2. The inference facts

  3. The asked facts


Removes the last visited rule and return its number.

visit_rule($rule, $total_causes)

Adds the given $rule to the end of the visited_rules.

copy_to_goals_to_check($rule, $facts)

Copies a list of facts (usually a list of causes of a rule) to goals_to_check_dict.

The rule ID of the goals that are being copied is also stored in the hahs.


Uses viewer to ask the user for the existence of the given $fact.

The valid answers are:


In case user knows of it.


In case user doesn't knows of it.


In case user doesn't have any clue about the given fact.


Looks in the knowledge_db for the rule that has the given goal. If a rule is found its number is returned, otherwise undef.


    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']);

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.


    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']);

The backward algorithm starts with a set of 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 is_goal_in_our_facts()) for more information).

  • 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 goals_to_check_dict.

    In case there are still causes of this rule in 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.

  • 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 (visited_rules) and also all of its causes will be added to the top of the 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 ask_about()) for the existence of it. If user is not sure about it then the algorithm ends.


As its name says, it's a mix of forward() and backward() algorithms, it requires to have at least one initial fact.

The first thing it does is to run the 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 run of 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 certainty factor of its causes versus the initial, inference and asked facts. In case the certainity factor is greater or equal than 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 backward() algorithm for each one of these facts (eg, each fact will be converted to a goal). After each run of the 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 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 X disease). However in case there's still no clue, then a set of questions (done by the call of 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 related symptons will be asked to the user.


The main purpose of any expert system is the ability to explain: what is happening, how it got to a result, what assumption(s) it required to make, the fatcs that were excluded and the ones that were used.

This method will use the viewer (or return the result) in YAML format of all the rules that were shot. It will explain how it got to each one of the causes so a better explanation can be done by the viewer.

If $return is defined (eg, it got any parameter) then the result wont be passed to the viewer, instead it will be returned as a string.


Take a look AI::ExpertSystem::Simple too.


Pablo Fischer (


Copyright (C) 2010 by Pablo Fischer.

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.