This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes: What do we mean by Expert? How do experts work? Expert Systems Application Domains, and Features of rule based Expert (KB) Systems

1. 1
Key Expert Systems Concepts
Harmony Kwawu
hkwawu@aol.com
1

2. Knowledge Base
Systems

3. Topics Covered
What is an expert?
What are expert sysems in the context of A.I?
Charactertistics of Expert Systems
Componets of Expert Sustems
Expert Systems Apllication Domains including:
Medcine
Engineering
Science
Business
LAW
Rule base Expert System
What is meant by Fact in Expert System
What are rules
Examples of how Facts and Rules are defned and
stored

4. Introduction
It’s more than 60years ago when Alan Turing, the great
British mathematician designed a test to judge whether
machines can outwit human beings.
The test which is used by many experts in the field of A.I
today, was designed to investigate whether people can
detect if they are talking to a machine or human.
The machine is declared a winner, if it manages to convince
more than 30% of human judges into believing that it is
not a machine but a human.
Currently, it’s not possible to conclusively say that
machines have developed the ability to think. But since
Alan Turing’s experiment, the development of smart
machines capable of outperforming humans in certain
domains have been growing.

5. Introduction Con’t
This follow up post on the subject of Artificial Intelligence
will focus on discussing Expert systems and the role of
traditional experts in their design and development.
We shall explore in particular four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Apllication Domains, and
Features of rule based Expert (KB) Systems

6. What do we mean by Expert?

7. What do we mean by Expert?
The term expert is over used nowadays to the extend that it’s at risk of
losing its true meaning and a lot more.
During the June Brexit referendum campaign for example, Michael Gove,
the then secretary of state for justice declared “people in this country have
had enough of experts,” this piece is not about whether experts should be
trusted or not. But to introduce the basic concept of rule based expert
systems and their use cases
For a start, experts are people with deep knowledge of specialist fields
and skills gained through many years of practise and learning
Put another way, an expert is a person or an artificial agent with special
knowledge and skills gained after many hours of training and practice.

8. Taking it further
The online business dictionary definition of an expert is
perhaps the most comprehensive. It define expert as:
A professional who has acquired knowledge and skills
through study and practice over many years, in a particular
field or subject, to the extent that his or her opinion may
be helpful in fact finding, problem solving, or understanding
of a situation.
Experts are not only highly trained individuals, they reflect
on their practice, continue to study and keep their
knowledge up-to-date

9. Experts are Knowledge Workers

10. Knowledge Workers
People who use their knowledge to create, use and share
information, are known as knowledge workers. To name
but a few:
Lawyers, including barristers and solicitors who use
their knowledge of law to advice and defend their
clients
Doctors use their knowledge of how the body works,
diagnostics techniques and understanding of medicine
to cure patients
Information systems experts use their knowledge of
information technology (Hardware and software) to
design new systems that improve business process and
make life better for end users

11. Knowledge Workers
Expert Equation= Lots of facts + use of deductive logic +
good understanding of how to solve complex problems

12. Question?
Is it possible to fully automate
human expertise?
More specifically, can the skills and
knowledge of a human expert be
captured and encoded?

13. What then is an Expert System?

14. Expert Systems
Again this has many different meaning, but to keep
it simple, an expert system is an artificial intelligence
(AI) application that emulate human traits and
perform complex tasks as a human expert would.
Expert Systems are Knowledge Base Information
Systems designed in most cases to offer support to
human users in a particular field-medicine, law,
insurance, etc
That means knowledge in an expert system is
domain specific and consist of facts and rules about
the domain
Like a human expert, experts systems are used in
knowledge discovery, solving complex problems, and
aiding humans in understanding complex situation.

15. Input from human Experts are crucial

16. Input from human experts are crucial
The success of an expert system project depends on the
quality of facts (data) and rules obtained from a human
expert or users.
We shall consider this in more details when discussing
knowledge representation repository design in a future
post

17. Characteristics of Expert Knowledge

18. Characteristics of an Expert Knowledge
Expert systems are different from traditional information
systems.
They do more than just capture, process and store
information. Experts Systems are capable of building on
human knowledge, experience and discover new things on
their own
They are therefore expected to be amongst other things,
highly reliable, flexible, efficient and provide a clear
explanation of decision, (Giarratano and Riley, 2007)

19. Characteristics of Expert Knowledge
An effective expert system is:
Capable of responding at a level of competency equal to
or better than a human expert
Able to perform in a reasonable time, comparable to or
better than human
Able to explain the steps of its reasoning to the end
user
Built with facilities to enable users add, change, and
replace dated knowledge

20. Components of Expert System

21. Components of Expert System
Expert systems are made up of four major
components:
Knowledge base
Inference Engine
Interactive user interface, and
An actives working memory

22. Components of Expert System
Working memory
fact base
Knowledge
base
Inference /Interface
Engine
Subject Experts Knowledge Engineer ES Developer
End user
With
computer &
interface
Receives
expert advice
Ask question
or query

23. How the various components work
together
23

24. How they work
Expert systems solves problems or provide answers to
users questions by:
Comparing rules and facts stored in knowledge base and
facts in memory respectively to produce result
The main component that does the comparing or reasoning
is called the inference engine.
Any new fact obtained from users are added to the
database and used to train the system for better
performance

25. Expert System Application

26. Areas of use
Knowledge base systems are used in many
fields, including:
Medical diagnostics
Engineering equipment repair
Financial decision, e.g. investment analysis
Estate and insurance planning
Transport, vehicle driver routing and
navigation
Manufacturing production control and
Education and training

27. Real world examples

28. Application of expert systems to
business problems
Applications of expert systems technology to a wide variety
of specific knowledge fields and problems
See this list by World Technology Evaluation Center (WTEC),
Division of Loyola University Maryland:
http://www.wtec.org/loyola/kb/c1_s2.htm

29. Expert systems Application
Artificial Neural Network
Automatic Self
service

30. The development of expert systems
is a team effort
How are they developed?

31. The Development Team
Team members involve in a typical expert system
development project:
Domain subject experts
Knowledge engineer or Analyst
System Engineer or developer
Expert System user

32. Why Expert Systems

33. Are Expert Systems better than
human experts?

34. Are Expert Systems better?
The debate about performance gap between human
experts versus intelligence machines is on going
The limit of human capacity to make decision and solve
problem pose great challenges to the wellbeing and
prosperity of society.
Expert System can be programmed to run on their own
logic and intelligence.
More often than not humans act out of emotion, insecurity,
irrational thoughts, and personal beliefs that confuses how
the world work with how they think the world ought to
work.

35. But does that makes them better than
human experts?
I am not sure,
what do you
think?

36. What are Rules and Facts in Expert
Systems

37. What are Rules and Facts in Expert
Systems
The most important component of an expert
system is perhaps the knowledge base
Knowledge base comprises of:
Domain specific data or fact, often expressed
as a condition
Rules for reasoning and for accomplishing task
in the specific domain (condition & outcome)
Both are structured so that they can aid
reasoning

38. What is factual statement
Change the Facts not the Rules, says the
great sage

39. What are facts and How do we know?
A fact is something that has really occurred or is
actually the case and known to be true.
A statement is factual if it can be validated and
verified
The usual test for a statement of fact is whether
it can be demonstrated to correspond to
experience.

40. Examples of random factual statements
Most mammals are hairy
Humans are mammals
All mammals are worm blooded
January is the first month of the year
the size of middle class in Africa is growing
Sleep is necessary
Humans need food to stay alive

41. Examples of random factual statements
Students who hand in their work late score less
mark
Immigration visa is issued to applicants who
score more than 60 points
Over draft fees are charged to customers who
exceed an agreed limit
The pass mark for BCS Exam is 40%
BCS exams this year is in March

42. How can you be sure of your facts?
How would you check a statement for its
accuracy and validity?

43. Expert Systems Rules
What do we mean by Rules in an
Expert System?

44. Rules
In problem solving or artificial intelligence, rules are forms
of knowledge expression.
A way to express understanding of what the facts are and
how they apply in practice
Rules provide some description of how to solve a problem
or perform a task
Rules are the popular paradigm for representing
knowledge.

45. The Building Block of rules
Rules are made of two main parts, namely an
“IF….” part and “THEN….” Part. Condition of an
event, real world object on one hand and possible
outcome on the other
A rule based expert system is one whose
knowledge base contains the domain knowledge
encoded in the form of rules.

46. Rules for various fields of knowledge
Can you work out the Rules for the use
cases below:
Making a scientific discovery
Data mining and knowledge discovery
Health promotion campaign
Solving a complex mathematical problem
Teaching someone how to learn effectively
Deducing intelligent behaviour
Building a new computer software
Solving a complex crime, like inspector
Colombo would

47. Rules outline how work is done or how to
solve a problem

48. Thinking in Rules

49. Thinking in Rules
Rules base thinking can be applied to:
Situations and expected actions
Premises and conclusions, and
Antecedents and consequences

50. Situations and Expected Actions
Situation refers to a condition or an event
Action by definition is what should be done if the
condition is satisfied
A simple illustrated example:
IF traffic light shows red Then stop, do not cross the
road.
IF total point scored < 60 THEN Reject visa
Application
IF customer account is >=10% over drawn THEN
charge Overdraft fee

51. Premise and Possible Conclusions
Premise is defined as a statement or an idea on
which a reason is based
The statement may be true or false
Conclusion, could referred to as end result or a
final point

52. Premise and Possible Conclusions
Example
IF student continue to absent themselves
from class THEN it means they have other
interfering activity
If the account holder has low credit score
THEN they are high risk, their loan
application should be rejected
If the sky is cloudy THEN it is likely to rain
If a product is scarce THEN sellers will
charge more for it

53. Antecedent and Consequences
The term Antecedent refers to a thing or event
that exist or occur before another. Put simply,
it’s simply, ancestors to a person, an object,
event or process
Remember, generalisation and specialisation
Consequences may be defined as: The outcome
of a course of action. E.g. the outcome of an
event

54. Antecedent and Consequences
Example:
IF x is a dog then x is an animal.
IF student attendance <=70% THEN they
would not pass their exam
IF x is a girl THEN x is a Female
IF x head injury is severe THEN x should be
rushed to emergency hospital
IF y is fitted with complicated systems THEN it
will cost more to repair it

55. Thanks for your attention
Keep in touch and let me know what you think

56. To find out more:
Peter Jackson, Introduction to Expert Systems,
Addison-Wesley (3rd Ed), 1998, ISBN 0201876868978-0201876864
Alison Cawsey, The Essence of Artificial Intelligence, ISBN-13: 978-
0135717790 ISBN-10: 0135717795
Pedro Domingos, (2015) The Master Algorithm: How the Quest for the
Ultimate Learning Machine Will Remake Our World

57. To find out more:
http://www.bcs.org/upload/pdf/pgdkbssyll.pdf
http://www.businessdictionary.com/definition/expert.html#ixzz3RJxhhhlK
http://users.cs.cf.ac.uk/Dave.Marshall/AI1/mycin.html
http://www.codeproject.com/Articles/179375/Man-Marriage-and-Machine-
Adventures-in-Artificia
MYCIN Artificial intelligence program
https://www.britannica.com/technology/MYCIN
https://www.deepdyve.com/browse/journalslp/expert-
systems?gclid=CJ2LrojGjNACFUoW0wodwXIFhQ