1. Artificial Intelligence CSC 361
Prof. Mohamed Batouche
Computer Science Department
CCIS – King Saud University
Riyadh, Saudi Arabia

2. Intelligent Systems Part I: Expert Systems

3. Developing Intelligent Program Systems
Two main approaches for problem solving using knowledge:
Symbolic Approach
Expert systems: Feed the system with knowledge. There is no learning !!
Connectionist Approach
Neural Networks: The system learns from examples by itself.

4. Symbolic Approach

5. Developing Intelligent Program Systems
Expert system
expert system (or knowledge-based system): a program which encapsulates knowledge from some domain, normally obtained from a human expert in that domain
an expert system can, to a certain extent, act as a substitute for the expert from whom the knowledge was taken.
Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems
Expert Systems Do Not Replace Experts, But They
Make their Knowledge and Experience More Widely Available
Permit Nonexperts to Work Better
ES acts as a Consultant or Advisor

6. Developing Intelligent Program Systems
Objective and Activities of an Expert System
Objective of an expert system
To transfer expertise from an expert to a computer system and
Then on to other humans (nonexperts)
Activities
Knowledge acquisition
Knowledge representation
Knowledge inferencing
Knowledge transfer to the user
Knowledge is stored in a knowledge base

7. Developing Intelligent Program Systems
Inferencing
Reasoning (Thinking)
The computer is programmed so that it can make inferences
Performed by the Inference Engine

8. Developing Intelligent Program Systems : Expert System
Major Components
Knowledge base (KB): repository of rules, facts (productions)
working memory: (if forward chaining used)
inference engine: the deduction system used to infer results from user input and KB
user interface: interfaces with user
external control + monitoring: access external databases, control,...
User Interface
Inference
Engine
Knowledge
Base

9. Developing Intelligent Program Systems : Expert System
Knowledge base
The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems
Two Basic Knowledge Base Elements
Facts
Special heuristics, or rules that direct the use of knowledge
Knowledge is the primary raw material of ES
Incorporated knowledge representation
User Interface
Inference
Engine
Knowledge
Base

10. Developing Intelligent Program Systems : Expert System
Inference Engine
The brain of the ES
The control structure (rule interpreter)
Provides methodology for reasoning
User Interface
Inference
Engine
Knowledge
Base

11. Developing Intelligent Program Systems : Expert System
User Interface
Language processor for friendly, problem-oriented communication
Natural Language Processing, or menus and graphics
User Interface
Inference
Engine
Knowledge
Base

12. Developing Intelligent Program Systems : Expert System
Working Memory (Blackboard)
Area of working memory to
Describe the current problem
Record Intermediate results
Records Intermediate Hypotheses and Decisions
1. Plan
2. Agenda
3. Solution
User Interface
Inference
Engine
Knowledge
Base

13. Developing Intelligent Program Systems : Expert System
Explanation Subsystem (Justifier) Area of working memory to
Traces responsibility and explains the ES behavior by interactively answering questions:
-Why?
-How?
-What?
-(Where? When? Who?)
User Interface
Inference
Engine
Knowledge
Base

14. Developing Intelligent Program Systems : Expert System
The Human Element in Expert Systems
Expert
Knowledge Engineer
User
Others

15. Developing Intelligent Program Systems : Expert System
Has the special knowledge, judgment, experience and methods to give advice and solve problems
Provides knowledge about task performance

16. Developing Intelligent Program Systems : Expert System
Knowledge Engineer
Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties
Usually also the System Builder

17. Developing Intelligent Program Systems : Expert System
User
Possible Classes of Users
A non-expert client seeking direct advice (ES acts as a Consultant or Advisor)
A student who wants to learn (Instructor)
An ES builder improving or increasing the knowledge base (Partner)
An expert (Colleague or Assistant)
The Expert and the Knowledge Engineer Should Anticipate Users' Needs and Limitations When Designing ES

18. Expert System – in Depth
Expert System Architecture
Knowledge Base
Inference Engine

19. Architecture of an Expert System

20. Knowledge base: production System
Knowledge is represented using rules of the form :
Rule: if Conditions then Conclusions or
Rule: if Premises then Actions
Rule: if if-part then then-Part
A rule as described above is often referred to as a production rule
Example: if symptom1 and symptom2 and symptom3 then disease1

21. Knowledge base: production System
Other examples:
if - the leaves are dry, brittle and discoloured
then - the plant has been attacked by red spider mite
If – it is raining
then – you should take an umbrella
if - the customer closes the account
then - delete the customer from the database

22. Inference Engine: Rule-based reasoning
The essence of a rule-based reasoning system is that it goes through a series of cycles.
In each cycle, it attempts to pick an appropriate rule from its collection of rules, depending on the present circumstances, and to use it.
Because using a rule produces new information, it's possible for each new cycle to take the reasoning process further than the cycle before. This is rather like a human following a chain of ideas in order to come to a conclusion.

23. Inference Engine: Forward Chaining
Forward Chaining is based on Modus Ponens inference rule:
(A, AB ) / B
In other words, if A is true and we have AB then we can deduce that B is true
In the context of Expert System, it is used as follows:
if A is in WM and we have a rule in KB of the form if A then B then we can deduce B (add B to WM as new information)

24. Inference Engine: Forward Chaining
Do until problem is solved or no antecedents match
Collect the rules whose antecedents are found in WM.
If more than one rule matches
use conflict resolution strategy to eliminate all but one
Do actions indicated in by rule “fired”
Cycles

25. Inference Engine: Forward Chaining
For Conflict Resolution we can use rule-order as an implied priority
Matching
Rules filtering
Conflict Resolution
Execution
Apply the rule
Rules
Rule
Add Then-Part to WM
Cycles

26. Inference Engine: Forward Chaining
Matching
Rules filtering
Conflict Resolution
Execution
Apply the rule
Rules
Rule
Add Then-Part to WM
Algorithm:
Match WM with KB to select production rules
Eliminate already applied rules
If many rules select one which has the smallest number
Apply selected rule by adding its conclusion to WM
If the problem is solved or no new information added then stop otherwise go to step 1

27. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B

28. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C

29. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2: Rule 5, Rule 1, Rule 2 → add D
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D

30. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2: Rule 5, Rule 1, Rule 2 → add D
Cycle 3: Rule 1, Rule 2, R 3, R 4, Rule 5 → add G
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D, G

31. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2: Rule 5, Rule 1, Rule 2 → add D
Cycle 3: Rule 1, Rule 2, R3, R 4, Rule 5 → add G
Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D, G, F

32. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2: Rule 5, Rule 1, Rule 2 → add D
Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G
Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F
Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D, G, F, E

33. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2: Rule 5, Rule 1, Rule 2 → add D
Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G
Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F
Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E
Cylce 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D, G, F, E, Z

34. Inference Engine: Forward Chaining
Inference Engine: Execute many cycles
Cycle 1: Rule 5 → add C
Cycle 2:Rule 1, Rule 2, Rule 5 → add D
Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G
Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F
Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E
Cylce 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z
Cycle 7: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → no rule => STOP !!!
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B, C, D, G, F, E, Z

35. Inference Engine: Backward Chaining
Backward Chaining is based on Modus Tollens inference rule:
( B, AB ) /  A
If A implies B , and B is false, then A is false
In other words, if we have AB then proving B is equivalent of proving A
In the context of Expert System, it is used as follows:
To prove a goal B which is not in WM and if we have a rule in KB of the form if A then B then we have just to prove A (A is either present in WM or it exists a rule with A as a conclusion)

36. Inference Engine: Backward Chaining
Consider the following KB and WM:
Inference Engine: Exploration of AND/OR Tree:
Suppose we have the goal: G
To prove G, one can use Rule 2
Proving G is equivalent of proving B and C
As B is present in the WM so it’s true
So remains the proof of C, one can use Rule 5
Proving C is equivalent of proving A and B
As A and B are present in WM (true), we can deduce that C is true
B and C are true then G is true
Knowledge Base:
Rule 1: If A and B and C then D
Rule 2: if B and C then G
Rule 3: if B and D then F
Rule 4: if B and C and D then E
Rule 5: if A and B then C
Rule 6: if G and D then Z
Working Memory: A, B

37. Conclusions Expert System Why use expert systems: Weaknesses:
commercial viability: whereas there may be only a few experts whose time is expensive and rare, you can have many expert systems
expert systems can be used anywhere, anytime
expert systems can explain their line of reasoning
commercially beneficial: the first commercial product of AI
Weaknesses:
expert systems are as sound as their KB; errors in rules mean errors in diagnoses
automatic error correction, learning is difficult
the extraction of knowledge from an expert, and encoding it into machine-inferable form is the most difficult part of expert system implementation