1. Introduction to Knowledge-based Systems
ดร.มารุต บูรณรัช
269618: หัวข้อพิเศษด้านเทคโนโลยีสารสนเทศขั้นสูง - เทคโนโลยีเว็บเชิงความหมาย (Special Topics in Advanced Information Technology – Semantic Web Technology)
ภาควิชาวิทยาการคอมพิวเตอร์และเทคโนโลยีสารสนเทศ
คณะวิทยาศาสตร์ มหาวิทยาลัยนเรศวร
ภาคการศึกษาที่ 2 ปีการศึกษา 2557
These slides are taken with some adaptations from Expert Systems: Principles and Programming, 4th Edition by Joseph C. Giarratano & Gary D. Riley

2. Semantic Web Stack
Adapted from

3. Outline Introduction to Expert Systems
History
Components
Functions and Applications
Expert systems vs. Imperative programming
Knowledge Representation and Logic
Propositional logic
Predicate logic
Inference Methods

4. Introduction to Expert Systems

5. Artificial Intelligence
AI = “Making computers think like people.”
Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning reasoning, solving problems and so on.
Barr and Feigenbaum, 1981
Expert Systems: Principles and Programming, Fourth Edition

6. Areas of Artificial Intelligence
Expert Systems: Principles and Programming, Fourth Edition

7. 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

8. More definitions
“An expert system is a program that attempts to mimic human expertise by applying inference methods to a specific body of knowledge.” (Darlington, 2000)
“An expert system is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise.” (Turban, 2001)

9. 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

10. 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

11. Basic Functions of Expert Systems
Expert Systems: Principles and Programming, Fourth Edition

12. 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

13. Problem Domain vs. Knowledge Domain (2)
Example: infections diseases diagnostic system does not have (or require) knowledge about other branches such as surgery.
Expert Systems: Principles and Programming, Fourth Edition

14. Problem and Knowledge Domain Relationship
Expert Systems: Principles and Programming, Fourth Edition

15. Advantages of Expert Systems
Increased availability
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

16. Advantages of Expert Systems (2)
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

17. 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

18. 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

19. Knowledge Engineering
The process of building an expert system:
The knowledge engineer establishes a dialog with the human expert to elicit knowledge.
The knowledge engineer codes the knowledge explicitly in the knowledge base.
The expert evaluates the expert system and gives a critique to the knowledge engineer.
Expert Systems: Principles and Programming, Fourth Edition

20. Development of an Expert System
Expert Systems: Principles and Programming, Fourth Edition

21. The Role of AI
Expert system relies on inference – we accept a “reasonable solution.”
Expert Systems: Principles and Programming, Fourth Edition

22. 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

23. Limitations of Expert Systems (2)
Limitation 2: typical expert systems cannot generalize through analogy to reason about new situations in the way people can.
Solution: repeating the cycle of interviewing the expert.
Limitation: 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

24. 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

25. Broad Classes of Expert Systems
Expert Systems: Principles and Programming, Fourth Edition

26. 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

27. 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

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

29. Elements of an Expert System (2)
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

30. Structure of a Rule-Based Expert System
Expert Systems: Principles and Programming, Fourth Edition

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

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

33. Inference engine operates on recognize-act cycle (2)
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

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

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

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

37. 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

38. Procedural Languages
Expert Systems: Principles and Programming, Fourth Edition

39. 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

40. 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

41. Nonprocedural Languages
Expert Systems: Principles and Programming, Fourth Edition

42. 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

43. Knowledge Representation & Logic

44. What is the study of logic?
Logic is the study of making inferences
given a set of facts, we attempt to reach a true conclusion.
An example of informal logic is a courtroom setting where lawyers make a series of inferences hoping to convince a jury / judge.
Formal logic (symbolic logic) is a more rigorous approach to proving a conclusion to be true / false.
Expert Systems: Principles and Programming, Fourth Edition

45. Why is Logic Important We use logic in our everyday lives
“should I buy this car?”, “should I seek medical attention?”.
People are not very good at reasoning because they often fail to separate word meanings with the reasoning process itself.
Semantics refers to the meanings we give to symbols.
Expert Systems: Principles and Programming, Fourth Edition

46. The Goal of Expert Systems
We need to be able to separate the actual meanings of words with the reasoning process itself.
We need to make inferences w/o relying on semantics.
We need to reach valid conclusions based on facts only.
Expert Systems: Principles and Programming, Fourth Edition

47. Knowledge vs. Expert Systems
Knowledge representation is key to the success of expert systems.
Expert systems are designed for knowledge representation based on rules of logic called inferences.
The process of reaching valid conclusions is referred to as logical reasoning.
Expert Systems: Principles and Programming, Fourth Edition

48. How is Knowledge Used?
Knowledge has many meanings – data, facts, information.
How do we use knowledge to reach conclusions or solve problems?
Heuristics refers to using experience to solve problems – using precedents.
Expert systems may have hundreds / thousands of micro-precedents to refer to.
Expert Systems: Principles and Programming, Fourth Edition

49. A Priori Knowledge “That which precedes” Independent of the senses
Universally true
Cannot be denied without contradiction
Expert Systems: Principles and Programming, Fourth Edition

50. A Posteriori Knowledge
“That which follows”
Derived from the senses
Not always reliable
Deniable on the basis of new knowledge w/o the necessity of contradiction
Expert Systems: Principles and Programming, Fourth Edition

51. Knowledge in Rule-Based Systems
Knowledge is part of a hierarchy.
Knowledge refers to rules that are activated by facts or other rules.
Activated rules produce new facts or conclusions.
Conclusions are the end-product of inferences when done according to formal rules.
Expert Systems: Principles and Programming, Fourth Edition

52. Expert Systems vs. Humans
Expert systems infer – reaching conclusions as the end product of a chain of steps called inferencing when done according to formal rules.
Humans reason
Expert Systems: Principles and Programming, Fourth Edition

53. Expert Systems vs. ANS
Artificial Neural System (ANS) does not make inferences but searches for underlying patterns.
Expert systems
Draw inferences using facts
Separate data from noise
Transform data into information
Transform information into knowledge
Expert Systems: Principles and Programming, Fourth Edition

54. Productions
A number of knowledge-representation techniques have been devised:
Rules
Semantic nets
Frames
Scripts
Logic
Conceptual graphs
Expert Systems: Principles and Programming, Fourth Edition

55. Logic and Sets Knowledge can also be represented by symbols of logic.
Logic is the study of rules of exact reasoning – inferring conclusions from premises.
Automated reasoning
Expert Systems: Principles and Programming, Fourth Edition

56. Forms of Logic
Earliest form of logic was based on the syllogism – developed by Aristotle.
Syllogisms – have two premises that provide evidence to support a conclusion.
Example:
Premise: All cats are climbers.
Premise: Garfield is a cat.
Conclusion: Garfield is a climber.
Expert Systems: Principles and Programming, Fourth Edition

57. Venn Diagrams
Expert Systems: Principles and Programming, Fourth Edition

58. Syllogism Premise: All men are mortal Premise: Socrates is a man
Conclusion: Socrates is mortal
Only the form is important.
Premise: All X are Y
Premise: Z is a X
Conclusion: Z is a Y
Expert Systems: Principles and Programming, Fourth Edition

59. Categorical Syllogism
Syllogism: a valid deductive argument having two premises and a conclusion.
major premise: All M are P
minor premise: All S is M
Conclusion: All S is P
M middle term
P major term
S minor term
Expert Systems: Principles and Programming, Fourth Edition

60. Categorical Statements
Form
Schema
Meaning A
All S is P
universal affirmative E
No S is P
universal negative I
Some S is P
particular affirmative O
Some S is not P
particular negative
Expert Systems: Principles and Programming, Fourth Edition

61. Figure 1 2 3 4 Major Premise M P P M Minor Premise S M M S Mood
AAA-1 EAE-1 IAI-4
All M is P No M is P Some P is M
All S is M All S is M All M is S
All S is P No S is P Some S is P
Expert Systems: Principles and Programming, Fourth Edition

62. Syllogisms vs. Rules Syllogism: IF-THEN rule:
All basketball players are tall.
Jason is a basketball player.
=> Jason is tall.
IF-THEN rule:
IF All basketball players are tall and
Jason is a basketball player
THEN Jason is tall.
Expert Systems: Principles and Programming, Fourth Edition

63. Propositional Logic
Concerned with the subset of declarative sentences that can be classified as true or false.
We call these sentences “statements” or “propositions”.
Paradoxes – statements that cannot be classified as true or false.
Open sentences – statements that cannot be answered absolutely.
Expert Systems: Principles and Programming, Fourth Edition

64. Propositional Logic (2)
Compound statements – formed by using logical connectives (e.g., AND, OR, NOT, conditional, and biconditional) on individual statements.
Material implication
p  q states that if p is true, it must follow that q is true.
Biconditional
p  q states that p implies q and q implies p.
Expert Systems: Principles and Programming, Fourth Edition

65. Propositional Logic (3)
Syllogisms address only a small portion of the possible logical statements.
Propositional logic offers another means of describing arguments.

66. Rule of Inference Modus ponens Modus tollens Direct reasoning ‘Assert’
Indirect reasoning
‘Deny’
Expert Systems: Principles and Programming, Fourth Edition

67. Modus ponens If there is power, the computer will work There is power
The computer will work
A  B p, p ->q; q A B
Expert Systems: Principles and Programming, Fourth Edition

68. Direct Reasoning: Modus Ponens
Expert Systems: Principles and Programming, Fourth Edition

69. Modus tollens p  q conditional p -> q ~q converse q -> p
inverse ~p -> ~q
~ p contrapositive
~q -> ~p
Expert Systems: Principles and Programming, Fourth Edition

70. Limitations of Propositional Logic
If an argument is invalid, it should be interpreted as such – that the conclusion is necessarily incorrect.
An argument may be invalid because it is poorly created.
An argument may not be provable using propositional logic, but may be provable using predicate logic.
Expert Systems: Principles and Programming, Fourth Edition

71. Predicate Logic Predicates with arguments on-top-of(A, B)
Variables and Quantifiers
Universal (x)(Rational(x)  Real(x))
Existential (x)(Prime(x))
Functions of Variables
(x)(Satellite(x))  (y)(closest(y, earth)^on(y,x))
(x)(man(x)  mortal(x))
^ man(Socrates)
=> mortal(Socrates)
Expert Systems: Principles and Programming, Fourth Edition

72. Universal Quantifier
The universal quantifier, represented by the symbol  means “for every” or “for all”.
(x) (x is a rectangle  x has four sides)
The existential quantifier, represented by the symbol  means “there exists”.
(x) (x – 3 = 5)
Limitations of predicate logic – most quantifier.
Expert Systems: Principles and Programming, Fourth Edition

73. First Order Predicate Logic
Quantification not over predicate or function symbols
No MOST quantifier, (counting required)
Can not express things that are sometime true
=> Fuzzy Logic
Expert Systems: Principles and Programming, Fourth Edition

74. Syllogism in Predicate Logic
Type Scheme Predicate Representation
A All S is P (x)(S(x) -> P(x))
E No S is P (x)(S(x) -> ~P(x))
I Some S is P (x)(S(x) ^ P(x))
A Some S is not P (x)(S(x) ^ ~P(x))
Expert Systems: Principles and Programming, Fourth Edition

75. Rule of Universal Instantiation
The Rule of Universal Instantiation states that an individual may be substituted for a universe.
(x)p(x) => p(a) p: any proposition or
propositional function
a: an instance
Expert Systems: Principles and Programming, Fourth Edition

76. Formal Proof (x)(H(x)->M(x)) All men are mortal
H(s) Socrates is a man
M(s) => Socrates is mortal
(x)(H(x)->M(x)) premise
H(s)
H(s)->M(s) universal instantiation
4. M(s) 2,3 modus ponens
Expert Systems: Principles and Programming, Fourth Edition

77. Summary Expert systems or Knowledge-based systems
captured human knowledge in a computer
apply inference methods to the knowledge
solve problems that normally required human expertise
Expert systems uses declarative language (rules) rather than procedural language (algorithms)
Expert Systems: Principles and Programming, Fourth Edition

78. Summary
It is necessary to specify formal rules for expert systems to be able to reach valid conclusions from given facts.
Propositional logic
Rules of inference
Determine true of false of statements (proposition)
Predicate logic or First-order logic
Formula that can contains variables that can be quantified
Can allow for formal proof