2. Introduction to Expert Systems Session 1
Course : T0273 EXPERT SYSTEMS
Year : 2016
Introduction to Expert Systems Session 1

3. Learning Outcomes
After taking this course, students should be able to explain and discuss the importance of Expert Systems.
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4. Lecture Outline What is an Expert System Advantages of Expert Systems
State of the Art
Characteristics of An Expert System
Application of the Expert Systems
Elements of the Expert Systems
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5. Lecturer Dr. Widodo Budiharto HP: 08569887384
Quiz 3x, TM dan Final project di sessi 13
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6. Introduction https://www.youtube.com/watch?v=cf6xSx2d6ts
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7. What is An Expert System
The earliest popular definition of AI is : “Making computers think like people”
Expert systems were developed as research tools in the 1960s as a special type of AI to successfully deal with complex problem sin a narrow domain such as medical disease diagnosis.
An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.
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8. AI Prehistory
Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality
Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability
Economics utility, decision theory
Neuroscience physical substrate for mental activity
Psychology phenomena of perception and motor control, experimental techniques
Computer building fast computers engineering
Control theory design systems that maximize an objective function over time
Linguistics knowledge representation, grammar
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9. History of AI 1943 McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted
1952—69 Look, Ma, no hands!
1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
1965 Robinson's complete algorithm for logical reasoning
1966—73 AI discovers computational complexity Neural network research almost disappears
1969—79 Early development of knowledge-based systems
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents
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10. State of the Art
Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)
During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people
NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft
Proverb solves crossword puzzles better than most humans
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11. USER Basic Function of an ES
The knowledge in expert systems (ES) may be either expertise, or knowledge that is generally available from books, magazines and knowledgeable persons.
Facts
Expertise
KNOWLEDGE-BASED
USER
INFERENCE ENGINE
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12. Advantages of ES Increased availability Reduced cost Reduced danger
Permanence
Multiple expertise
Increased reliability
Explanation
Fast response
Intelligent tutor
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14. Bina Nusantara University

15. Characteristics of an ES
High performance
Adequate response time
Good reliability
Understandable
Flexibility
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16. General concepts
The knowledge of an expert system may be represented in a number of ways. One common method of representing knowledge is in the form of IF THEN type rules, such as :
IF the car doesn’t run and
The fuel gauge reads empty
THEN fill the gas tank
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17. Interpret a protein’s 3D structure Interpret molecular structure
Applications
Interpret a protein’s 3D structure
Interpret molecular structure
Diagnose telephone network faults
Diagnose lug disease
Diagnose blood disease
Instruct in bacterial infection
Diagnose/remedy drilling problems
Diagnose bad parts in switching net.
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18. Elements of an Expert System
User Interface
Explanation facility
Working memory
Inference engine
Agenda
Knowledge acquisition facility
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19. Inference Engine
Makes inferences by deciding which rules are satisfied by facts or objects. Prioritizes the satisfied rules, and executes the rule with the highest priority
Merupakan otak dari sistem pakar, berupa perangkat lunak yang melakukan tugas inferensi pelanaran sistem pakar
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20. Knowledge acquisition facility
An automatic way for the user to enter knowledge in the system rather than by having the knowledge engineer explicitly code the knowledge
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21. Working Memory A global database of facts used by the rules
Menyimpan fakta yang diperoleh saat dilakukan proses konsultasi
Fakta fakta ini akan diolah oleh mesin inferensi berdasar pengetahuan yang disimpan di dalam basis pengetahuan untuk menentukan suatu keputusan pemecahan masalah
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22. Agenda
A prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory
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23. Examples of expert system applications, such as :
Loan evaluators and technical support systems
Farm Advisory System
Intelligent Tutoring systems (ITS) :
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24. example
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25. MYCIN – Medical Diagnosis ES
Diagnosis of meningitis and bacteremia
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26. Aplikasi Android ES
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27. 1. Gather information from experts. 2. Create the knowledge base.
Exercise
An expert system is being developed to help engineers diagnose faults in aero engines. Describe the steps taken to develop this new expert .
ANSWER:
1. Gather information from experts.
2. Create the knowledge base.
3. type/put information into computer .
4. Create rules / rules base.
5. Create / design inference engine.
6. Fully test the system.
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28. Summary
The problem that expert systems are used to solve are generally not solvable by conventional program.
The advantages and disadvantages of expert systems were also discussed in the context of selecting an appropriate problem domain for an expert systems application.
The essentials of an expert system shell were discussed with reference to rule-based expert systems.
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29. References Textbooks:
Joseph Giarratano, Gary Riley, Expert systems : principles and programming. THOCO. Australia. ISBN:
Stuart Russell Peter Norvig Artificial Intelligence, A Modern Approach. PE. New Jersey. ISBN:
Web :
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