This document provides an overview of expert systems, including their key components and development process. It defines expert systems as computer systems that emulate the decision-making of human experts in a particular domain. The main components are a knowledge base containing rules and facts, and an inference engine that applies rules to deduce new facts. The development process involves knowledge engineering to extract an expert's knowledge into a formal representation. Rule-based systems represent knowledge through IF-THEN production rules. The document also discusses the advantages and limitations of expert systems.
The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
Complete Presentation on Mycin - An Expert System. ,mycin - an expert system ,mycin ,mycin expert system ,mycin system ,mycin expert ,expert system mycin ,mycin presentation ,how mycin work ,mycin architecture ,components of mycin ,tasks of mycin ,how mycin became successful ,is mycin used today? ,user interface of mycin
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
Complete Presentation on Mycin - An Expert System. ,mycin - an expert system ,mycin ,mycin expert system ,mycin system ,mycin expert ,expert system mycin ,mycin presentation ,how mycin work ,mycin architecture ,components of mycin ,tasks of mycin ,how mycin became successful ,is mycin used today? ,user interface of mycin
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Rule-based systems are used as a way to store and manipulate knowledge to interpret information in a useful way. Often used in artificial intelligence applications and research.
The transformer is the most important equipment in the transmission and distribution system.
This expert system is the principle of condition based maintenance strategy. The system consider discrete diagnostical results.
For the comparison we need to consider some other parameters what do not indicate the status of the insulation but it has influence for that.
In the research work, the expert system tested by real data from the Hungarian distribution system. The source of the testing data is 13 HV/MV distribution transformers in Hungary
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
1. Chapter 1:
Introduction to
Expert Systems
Expert Systems: Principles and
Programming, Fourth Edition
2. Objectives
• Learn the meaning of an expert system
• Understand the problem domain and knowledge
domain
• Learn the advantages of an expert system
• Understand the stages in the development of an
expert system
• Examine the general characteristics of an expert
system
Expert Systems: Principles and Programming, Fourth Edition 2
3. Objectives
• Examine earlier expert systems which have given
rise to today’s knowledge-based systems
• Explore the applications of expert systems in use
today
• Examine the structure of a rule-based expert
system
• Learn the difference between procedural and
nonprocedural paradigms
• What are the characteristics of artificial neural
systems
Expert Systems: Principles and Programming, Fourth Edition 3
4. Artificial Intelligence
• AI = “Making computers think like people.”
Expert Systems: Principles and Programming, Fourth Edition 4
5. Areas of Artificial Intelligence
Expert Systems: Principles and Programming, Fourth Edition 5
6. What is an expert system?
“An expert system is a computer system
that emulates, or acts in all respects, with
the decision-making capabilities of a human
expert.”
Professor Edward Feigenbaum
Stanford University
• Expert Systems = knowledge-based systems
= knowledge-based expert systems
Expert Systems: Principles and Programming, Fourth Edition 6
7. What is an expert system?
• Emulation (mimics cause/process) is stronger than
simulation (mimics outward appearance) which is
required to act like the real thing in only some
aspects.
• The basic idea is that if a human expert can specify
the steps of reasoning by which a problem may be
solved, so too can an expert system.
• Restricted domain expert systems (extensive use of
specialized knowledge at the level of human expert)
function well which is not the case of general-purpose
problem solver.
Expert Systems: Principles and Programming, Fourth Edition 7
8. Expert system technology
may include:
• Special expert system languages – CLIPS
• Programs
• Hardware designed to facilitate the
implementation of those systems (e.g., in
medicine)
Expert Systems: Principles and Programming, Fourth Edition 8
9. Expert System Main Components
• Knowledge base – obtainable from books,
magazines, knowledgeable persons, etc; or
expertise knowledge.
• Inference engine – draws conclusions from the
knowledge base.
Expert Systems: Principles and Programming, Fourth Edition 9
10. Basic Functions of Expert Systems
Expert Systems: Principles and Programming, Fourth Edition 10
11. Problem Domain vs. Knowledge
Domain
• In general, the first step in solving any problem is
defining the problem area or domain to be solved.
• An expert’s knowledge is specific to one problem
domain – medicine, finance, science, engineering,
etc.
• The expert’s knowledge about solving specific
problems is called the knowledge domain.
• The problem domain is always a superset of the
knowledge domain.
• Expert system reasons from knowledge domain.
Expert Systems: Principles and Programming, Fourth Edition 11
12. Problem Domain vs. Knowledge
Domain
• Example: infections diseases diagnostic system
does not have (or require) knowledge about other
branches such as surgery.
Expert Systems: Principles and Programming, Fourth Edition 12
13. Problem and Knowledge
Domain Relationship
Expert Systems: Principles and Programming, Fourth Edition 13
14. Advantages of Expert Systems
• Increased availability: on suitable computer hardware
• Reduced cost
• Reduced danger: can be used in hazardous environment.
• Permanence: last for ever, unlike human who may die,
retire, quit.
• Multiple expertise: several experts’ knowledge leads to
• Increased reliability
Expert Systems: Principles and Programming, Fourth Edition 14
15. Advantages Continued
• Explanation: explain in detail how arrived at
conclusions.
• Fast response: (e.g. emergency situations).
• Steady, unemotional, and complete responses at all
times: unlike human who may be inefficient because
of stress or fatigue.
• Intelligent tutor: provides direct instructions (student
may run sample programs and explaining the system’s
reasoning).
• Intelligent database: access a database intelligently
(e.g. data mining).
Expert Systems: Principles and Programming, Fourth Edition 15
16. Representing the Knowledge
The knowledge of an expert system can be
represented in a number of ways, including IF-THEN
rules:
IF the light is red THEN stop
Expert Systems: Principles and Programming, Fourth Edition 16
17. Representing the Knowledge
Car Failure Diagnosis
IF the selection is 2 "Run-Stable State"
AND the fuel is not burning well
AND the engine running cycle is ok
AND there is no blue gas
AND the advance is bad
THEN
There is a Dirt in the injections/carburetor
or The adjustment of ear and gasoline is
not good, clear injections/carburetor and
adjust the ear.
Expert Systems: Principles and Programming, Fourth Edition 17
18. Knowledge Engineering
The process of building an expert system:
1. The knowledge engineer establishes a dialog
with the human expert to elicit knowledge.
2. The knowledge engineer codes the knowledge
explicitly in the knowledge base.
3. The expert evaluates the expert system and
gives a critique to the knowledge engineer.
Expert Systems: Principles and Programming, Fourth Edition 18
19. Development of an Expert System
Expert Systems: Principles and Programming, Fourth Edition 19
20. The Role of AI
• An algorithm is an ideal solution guaranteed to
yield a solution in a finite amount of time.
• When an algorithm is not available or is
insufficient, we rely on artificial intelligence
(AI).
• Expert system relies on inference – we accept a
“reasonable solution.”
Expert Systems: Principles and Programming, Fourth Edition 20
21. Limitations of Expert Systems
• Uncertainty = having limited knowledge (more
than possible outcomes)
• Both human experts and expert systems must be
able to deal with uncertainty.
• Limitation 1: most expert systems deals with
shallow knowledge than with deep knowledge.
• Shallow knowledge – based on empirical and
heuristic knowledge.
• Deep knowledge – based on basic structure,
function, and behavior of objects.
Expert Systems: Principles and Programming, Fourth Edition 21
22. Limitations of Expert Systems
• Limitation 2: typical expert systems cannot
generalize through analogy to reason about new
situations in the way people can.
• Solution 1 for limitation 2: repeating the cycle of
interviewing the expert.
• Limitation raised form Solution 1: A knowledge
acquisition bottleneck results from the time-consuming
and labor intensive task of building
an expert system.
Expert Systems: Principles and Programming, Fourth Edition 22
23. Early Expert Systems
• DENDRAL – used in chemical mass
spectroscopy to identify chemical constituents
• MYCIN – medical diagnosis of illness
• DIPMETER – geological data analysis for oil
• PROSPECTOR – geological data analysis for
minerals
• XCON/R1 – configuring computer systems
Expert Systems: Principles and Programming, Fourth Edition 23
24. Broad Classes of Expert Systems
Expert Systems: Principles and Programming, Fourth Edition 24
25. Problems with Algorithmic
Solutions
• Conventional computer programs generally solve
problems having algorithmic solutions.
• Algorithmic languages include C, Java, and C#.
• Classic AI languages include LISP and
PROLOG.
Expert Systems: Principles and Programming, Fourth Edition 25
26. Considerations for Building
Expert Systems
• Can the problem be solved effectively by
conventional programming? (expert systems are
suited for ill-structured problems- problems with no
efficient algorithmic solution)
• Is there a need and a desire for an expert system?
• Is there at least one human expert who is willing to
cooperate?
• Can the expert explain the knowledge to the
knowledge engineer in a way that can understand it.
• Is the problem-solving knowledge mainly heuristic
and uncertain?
Expert Systems: Principles and Programming, Fourth Edition 26
27. Languages, Shells, and Tools
• Expert system languages are post-third generation.
• Expert system languages (e.g. CLIPS) focus on
ways to represent knowledge.
• Tool = language + utility program (code generator,
graphics editor, debuggers, etc.).
• Shell: is a special purpose tool designed for certain
types of applications in which the user must supply
the knowledge base. Example, EMYCIN (empty
MYCIN)
Expert Systems: Principles and Programming, Fourth Edition 27
28. Elements of an Expert System
• User interface – mechanism by which user and
system communicate.
• Exploration facility – explains reasoning of
expert system to user.
• Working memory – global database of facts used
by rules.
• Inference engine – makes inferences deciding
which rules are satisfied and prioritizing.
Expert Systems: Principles and Programming, Fourth Edition 28
29. Elements Continued
• Agenda – a prioritized list of rules created by the
inference engine, whose patterns are satisfied by
facts or objects in working memory.
• Knowledge acquisition facility – automatic way
for the user to enter knowledge in the system
bypassing the explicit coding by knowledge
engineer.
Expert Systems: Principles and Programming, Fourth Edition 29
30. Structure of a
Rule-Based Expert System
Expert Systems: Principles and Programming, Fourth Edition 30
31. Production Rules
• Knowledge base is also called production
memory.
• Production rules can be expressed in IF-THEN
pseudocode format.
• In rule-based systems, the inference engine
determines which rule antecedents are satisfied
by the facts.
Expert Systems: Principles and Programming, Fourth Edition 31
32. Inference engine operates on
recognize-act cycle
While not done
conflict resolution:
act:
match:
check for halt:
End-while
Expert Systems: Principles and Programming, Fourth Edition 32
33. Inference engine operates on
recognize-act cycle
- conflict resolution: if there are activations then
select the one with the highest priority. Else
done.
- act: sequentially perform the actions. Update the
working memory. Remove the fired activations.
- match: Update the agenda by checking if there
are activation or remove activations if there LHS
is no longer satisfied.
- check for halt: if an halt action is performed or
break command given, then done.
Expert Systems: Principles and Programming, Fourth Edition 33
34. General Methods of Inferencing
• Forward chaining – reasoning from facts to the
conclusions resulting from those facts – best for
prognosis, monitoring, and control.
• Backward chaining – reasoning in reverse from a
hypothesis, a potential conclusion to be proved to
the facts that support the hypothesis – best for
diagnosis problems.
Expert Systems: Principles and Programming, Fourth Edition 34
35. Production Systems
• Rule-based expert systems – most popular type
today.
• Knowledge is represented as multiple rules that
specify what should/not be concluded from
different situations.
• Forward chaining – start w/facts and use rules do
draw conclusions/take actions.
• Backward chaining – start w/hypothesis and look
for rules that allow hypothesis to be proven true.
Expert Systems: Principles and Programming, Fourth Edition 35
36. Post Production System
• Basic idea – any mathematical / logical system is
simply a set of rules specifying how to change
one string of symbols into another string of
symbols.
• Basic limitation – lack of control mechanism to
guide the application of the rules.
Expert Systems: Principles and Programming, Fourth Edition 36
37. Markov Algorithm
• An ordered group of productions applied in order
or priority to an input string.
• If the highest priority rule is not applicable, we
apply the next, and so on.
• An inefficient algorithm for systems with many
rules.
Expert Systems: Principles and Programming, Fourth Edition 37
38. Rete Algorithm
• Functions like a net – holding a lot of
information.
• Much faster response times and rule firings can
occur compared to a large group of IF-THEN
rules which would have to be checked one-by-one
in conventional program.
• Takes advantage of temporal redundancy and
structural similarity.
• Drawback is high memory space requirements.
Expert Systems: Principles and Programming, Fourth Edition 38
39. Procedural Paradigms
• Algorithm – method of solving a problem in a
finite number of steps.
• Procedural programs are also called sequential
programs.
• The programmer specifies exactly how a problem
solution must be coded.
Expert Systems: Principles and Programming, Fourth Edition 39
41. Imperative Programming
• Focuses on the concept of modifiable store –
variables and assignments.
• During execution, program makes transition from
the initial state to the final state by passing
through series of intermediate states.
• Provide for top-down-design.
• Not efficient for directly implementing expert
systems.
Expert Systems: Principles and Programming, Fourth Edition 41
42. Nonprocedural Paradigms
• Do not depend on the programmer giving exact
details how the program is to be solved.
• Declarative programming – goal is separated
from the method to achieve it.
• Object-oriented programming – partly imperative
and partly declarative – uses objects and methods
that act on those objects.
• Inheritance – (OOP) subclasses derived from
parent classes.
Expert Systems: Principles and Programming, Fourth Edition 42
44. What are Expert Systems?
Can be considered declarative languages:
• Programmer does not specify how to achieve a
goal at the algorithm level.
• Induction-based programming – the program
learns by generalizing from a sample.
Expert Systems: Principles and Programming, Fourth Edition 44