!!! Printer Problems Diagnosis
This small demo example shows the basic functionality of KnowWE and its corresponding knowledge representations for the formalization of diagnostic knowledge.
We build a knowledge base for deriving the reasons for the faults of an imaginary printer.
[{TableOfContents title='Contents' numbered='true'}]
!!! Set-Up
!! Annotate Wiki Articles
Every article containing knowledge of the printer example needs to be tagged in a special manner.
We choose the tag name {{Printer_Demo}} and we insert the annotation
{{{
%%Package Printer_Demo
}}}
into every particular article containing knowledge.
!! Define the Knowledge Base Center
In general, the knowledge base can be distributed across many wiki articles (each of them tagged with {{Printer_Demo}}).
Therefore, we need to define a place where all knowledge elements are collected into a single knowledge base.
For this, we use the {{KnowledgeBase}} markup in the following manner:
{{{
%%KnowledgeBase
Printer Fault Diagnosis Demo
@author: joba
@version: 1.0
@uses: Printer_Demo
%
}}}
By inserting the markup the following center is provided:
%%KnowledgeBase
Printer Fault Diagnosis Demo
@author: joba
@version: 1.0
@uses: Printer_Demo
%
!!! Terminology
In the first step we need to define the terminology of the planned system, i.e., the inputs (user entries) and the outputs (derived solutions).
!! Solutions
We define the competence of the knowledge base by enumerating all possible solutions, i.e., faults, for the printer problem.
The derivable solutions are as follows:
# Connection problems
## Broken cable
## Cable not connected
## No Wifi connection
## No power supply
# Printing quality problems
## Empty toner
## Dirty toner
We see, that we have structured the solutions into meaningful groups, i.e. ''connection problems'' and ''printing quality problems''.
More groups can be added easily.
The structuring of solutions is especially useful when the knowledge base and the number of solutions, respectively, increases.
The d3web markup of the solution hierarchy is as follows:
{{{
%%Solution
Connection problems
- Broken cable
- Cable not connected
- No Wifi connection
- No power supply
Printing quality problems
- Empty toner
- Dirty toner
No solution found
%
}}}
Please note, that we added the solution ''No solution found'' for the case, that the system cannot derive an appropriate solution.
When inserting the markup into the wiki article, the following section is shown.
%%Solution
Connection problems
- Broken cable
- Cable not connected
- No Wifi connection
- No power supply
Printing quality problems
- Empty toner
- Dirty toner
No solution found
%
!! Questions
To derive the solutions we need to pose appropriate questions to the user.
Since, we expect many questions to be included in the knowledge base, we group the questions into meaningful questionnaires.
First, we define an initial questionnaire that tries to identify the problem area of the user and use the following markup:
{{{
%%Question
Problem specification #1
- Please specify your problem [oc]
-- Printer does not print
-- Printing with bad quality
-- No else
%
}}}
The annotation ''#1'' specifies, that the questionnaire ''Problem specification'' is posed as the initial questionnaire in a dialog session with the user.
When inserting the markup into the article, the questions are created.
%%Question
Problem specification #1
- Please specify your problem [oc]
-- Printer does not print
-- Printing with bad quality
-- No else
%
The annotation ''~[oc]'' specifies a corresponding one-choice question.
The possible values/answers of this question are printed in separate lines below the question.
Each value/answer should be indented by an increased dash.
We see, that the three answers ''Printer does not print'', ''Printing with bad quality'', and ''No else'' has been defined for the one-choice question ''Please specify your problem''.
Each problem area corresponds to a group of solutions.
That way, we define a questionnaire for each problem area in order to differentiate the solutions within the problem area.
! Problem Area: Connection Problems
The questions of the problem area ''connection problems'' are as follows:
%%Question
Connection Questions
- Please check the power supply of the printer [oc]
-- Printer is connected and power is on
-- Printer is not connected or power is off
- How is the printer connected to your computer [oc]
-- Connected by cable
-- Connected by Wifi
Printer with cable connection
- Is the cable plugged into the printer and the computer [yn]
-- Yes
-- No
- Please exchange the cable and try to print again [oc]
-- Stil not printing
-- Now it works
%
We see, that a number of one-choice questions (~[oc]) are defined.
Additionally, a yes/no question is defined (~[yn]), which is similar to one-choice questions, but has the predefined answers ''yes'' and ''no''.
! Problem Area: Quality Problems
The questions of the problem area ''quality problems'' are as follows:
%%Question
Quality Questions
- Dirty housing [yn]
- Toner capacity [num] {pages} (0 10000000)
- Paper count [num] {pages} (0 10000000)
-- Current capacity ratio [num] <abstract>
-- Toner usage evaluation [oc] <abstract>
--- normal
--- low
%
In addition to yes/no questions and one-choice questions we defined some numerical questions (~[num]), that allow the input of numerical values. The annotation ''(0 10000000)'' defines the value range of the particular question, i.e., numbers from 0 to 10000000 can be entered, whereas ''{pages}'' defines the unit as an arbitrary string, i.e., the number of ''pages'' is queried to the user.
The annotation ''<abstract>'' specifies a abstraction question.
For such a question the values are not entered externally, e.g., by a user in a dialog, but the values are computed internally by the knowledge base.
For the internal computation of the abstraction questions we define the following rules, by the {{Rule}} markup.
In the following, we define a rule computing the ratio between the expected toner life (''Toner capacity'') and the current use (''Paper count'').
Two further rules derive a ''normal'' or a ''low'' toner usage by using the derived value of the abstraction question ''Current capacity ratio'':
{{{
%%Rule
IF Toner capacity = KNOWN AND Paper count > 0
THEN Current capacity ratio = (Toner capacity / Paper count)
IF Current capacity ratio >= 1.1
THEN Toner usage evaluation = normal
IF Current capacity ratio < 1.1
THEN Toner usage evaluation = low
%
}}}
When inserted into the wiki article, the rules markup looks as follows:
%%Rule
IF Toner capacity = KNOWN AND Paper count > 0
THEN Current capacity ratio = (Toner capacity / Paper count)
IF Current capacity ratio >= 1.1
THEN Toner usage evaluation = normal
IF Current capacity ratio < 1.1
THEN Toner usage evaluation = low
%
! Human-friendly Prompts of Questions/Solutions
It is reasonable to define short names for questions and solutions because they are often used in the markups.
When used in the dialog, however, longer verbalizations may be useful.
You can define a longer prompt for questions/solutions by using the {{Property}} markup.
Moreover, the prompts can be internationalized.
%%Property
Dirty housing.prompt = When opening the housing of the printer: Is there some dirt at the toner and the surrounding?
Toner capacity.prompt = Number of pages usually printed with one toner
Paper count.prompt = Number of pages printed with the current toner
Paper count.prompt.de = Anzahl der mit den aktuellen Toner gedruckten Seiten
%
!!! Derivation Knowledge
After the definition of the terminology we need to define knowledge elements that implement the derivation and dialog behavior of the knowledge base.
For that, d3web/KnowWE provides a number of alternatives ranging from scoring rules, decision trees to set-covering models.
In this tutorial we show how to implement the derivation knowledge as a DiaFlux flowchart model.
* [DiaFlux implementation|Tutorial Printer Problem - DiaFlux]
!!! Testing the Knowledge Base
In the next step, we test the knowledge base by using the
%%todo Test the printer tutorial @user: joba
%%Package Printer_Demo