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.
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 it has at 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.
For making easier your job, AI::ExpertSystem::Advanced asks you only the id of the initial_facts. Once you provide them then a dictinary is created.
initial_facts
This initial_facts_dict dictionary basically provides a standard interface to get the sign of the facts.
initial_facts_dict
When doing the backward() algorithm it's needed to have at least one goal (aka hypothesis).
backward()
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).
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.
Very similar to goals_to_check (and indeed of initial_facts_dict). We want to make the job easier at the moment of assigning goals and based on this only the list of goals is needed, then a dictionary will be created with the data of goals_to_check.
goals_to_check
Inference facts are basically the core of an expert system. These are facts that are found and copied when the 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 the copy.
inference_facts
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.
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.
debug()
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).
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 fact) will be compared versus the value of this factor, if it's higher or equal then the rule will be triggered.
mixed()
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.
FACT_SIGN_NEGATIVE
Used when a fact is negative, aka, a fact doesn't happen.
FACT_SIGN_POSITIVE
Used for those facts that happen.
FACT_SIGN_UNSURE
Used when there's no straight answer of a fact, eg, we don't know if an answer will change the result.
Shoots the given rule. It will do the following verifications:
Each of the facts (causes) will be compared against the initial_facts_dict and asked_facts (in this order).
If an initial 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 initial_facts_dict and inference_facts with a negative sign, otherwise a positive sign will be used.
Will add the rule to the shot_rules hash.
shot_rules
Verifies if the given $rule has been shot.
$rule
Will ask the knowledge_db for the goals of the given $rule. A AI::ExpertSystem::Advanced::Dictionary will be returned.
knowledge_db
Will ask the knowledge_db for the causes of the given $rule. A AI::ExpertSystem::Advanced::Dictionary will be returned.
Will check if the given $fact of the given dictionary ($dict_name) is negative.
$fact
$dict_name
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.
$facts
$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.
$algorithm
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.
compare_causes_with_facts()
The match factor is used by the mixed() algorithm and is useful to know if a certain rule should be shoot or not.
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:
$goal
The asked facts
The inference facts
Removes the last visited rule and return its number.
Adds the given $rule to the end of the visited_rules.
visited_rules
Copies a list of facts (usually a list of causes of a rule) to <goals_to_check_dict>.
Uses viewer to ask the user for the existence of the given fact.
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.
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 facts and with the inference facts. If all of these causes are in our 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.
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 asked_facts and inference_facts.
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 take the last one and remove it. Then this visited rule will be shoot. Once the rule is shoot it verifies if there are still still more goals to check, if this is the case then it starts reading from the first goal (at this time the goals_to_check_dict is reduced by 1. However if there are no more goals to check then it will finish, making the end of the algorithm.
goals_to_check_dict
In case there are no more visited rules to shoot then it will finish making the end of the algorithm.
If the goal doesn't exist in the asked_facts or inference_facts then the goal will be searched in the list of goals of all the rules.
In case it finds the rule that has the goal, this rule will be marked (added) to the list of visited rules (visited_rules). Also all of the causes of this rule will be added to the top of the goals_to_check_dict. Once this is done it will start reading again all of the goals to check.
If there's the case where the goal doesn't exist as a goal in our 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.
ask_about
As its name says, it's a mix of forward() and backward() algorithms, it requires to have at least one initial fact.
forward()
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 was 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_facts_dict, inference_facts 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).
found_factor
Once all the rules are read then it verifies for any intuitive fact, 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 restarts (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 and diseases of a patient. Then the algorithm will start looking for any additional disease you could be missing. Then once it ends looking for diseases it will check if we know what the disease is (by looking for the positive fact). If there's still no clue then it starts looking in viceversa, now knowing a list of possible diseases and also a list of symptoms. It repeats all the process until a positive inference fact is found.
The main purpose of any expert system is the ability to explain: what is happening, how it got to a result, what assumption it required to make, which facts it excluded and which 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.
$return
Take a look AI::ExpertSystem::Simple too.
Pablo Fischer (pablo@pablo.com.mx).
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.
To install AI::ExpertSystem::Advanced, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::ExpertSystem::Advanced
CPAN shell
perl -MCPAN -e shell install AI::ExpertSystem::Advanced
For more information on module installation, please visit the detailed CPAN module installation guide.