1.
Expert System
BITS Pilani
Pilani Campus
Sagar Kr. Sharma

2.
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
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

3.
Artificial
intelligence
Vision
systems
Learning
systems
Robotics
Expert systems
Neural networks
Natural language
processing
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

4.
Expert System
 A branch of Artificial Intelligence that makes an
extensive use of specialized knowledge to solve
problems at the level of an human expert.
“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
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

5.
Why do we need Expert Systems
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Increased availability
Reduced Danger
Reduced Cost
Multiple expertise
Increased Reliability
Explanation facility
Fast Response
Steady, emotional & complete response
Intelligent tutor
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

6.
Complete structure of expert
system
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

7.
Expert System components
• Working Memory
– A global database of facts used by the system
• Knowledge Base
– Contains the domain knowledge
• Inference Engine
– The brain of the Expert system. Makes logical deductions based upon the
knowledge in the KB.
• User Interface
– A facility for the user to interact with the Expert system.
• Explanation Facility
– Explains reasoning of the system to the user
• Knowledge Acquisition Facility
– An automatic way to acquire knowledge
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

8.
Knowledge Types
•
The knowledge base of expert system contains both factual and heuristic
knowledge.
– Factual knowledge is that knowledge of the task domain that is widely shared,
typically found in textbooks or journals, and commonly agreed upon by those
knowledgeable in the particular field.
• The capital of Italy is Rome
• A day consists of 24 hours
• Bacteria type a causes Flu type B
– Heuristic knowledge is the less rigorous, more experiential, more judgmental
knowledge of performance.
• For instance, in a medical expert system - if patient has spots, it’s probably
chickenpox
• In a mechanical trouble shooting system - if engine doesn’t turn over,
check battery
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

9.
Inference engine cycles via a
match-fire procedure
Database
Fact: A is x
Fact: B is y
Match
Fire
Knowledge Base
Rule: IF A is x THEN B is y
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

10.
Conflict Resolution
• Conflict Resolution is a method that is used when more than one rule is
matched on the facts asserted. There are several approaches
– First in first serve
• It involves firing the first rule that matches the content of the
working memory or the facts asserted.
– Last in first serve
• The rule applied will be the last rule that is matched.
– Prioritization:
• The rule to apply will be selected based on priorities set on rules, with
priority information usually provided by an expert or knowledge engineer.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

11.
Conflict Resolution
• Specificity - The rule applied is usually the most
specific rule, or the rule that matches the most facts.
• Recency - The rule applied is the rule that matches the
most recently derived facts.
• Fired Rules - Involves not applying rules that have
already been used.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

12.
Conflict Resolution (example)
First we'll look at a very simple set of
rules:
1.
IF (lecturing X)
AND (marking- practicals X)
THEN ADD (overworked X)
2.
IF (month February)
THEN ADD (lecturing Alison)
3.
IF (month February)
THEN ADD (marking- practicals
Alison)
4.
IF (overworked X)
OR (slept-badly X)
THEN ADD (bad-mood X)
5.
IF (bad-mood X)
THEN DELETE (happy X)
6.
IF (lecturing X)
THEN DELETE (researching X)
7.
IF (marking – praticals X)
THEN ADD(Needsrest X)
Here we use capital letters to indicate
variables
(month February)
(researching Alison)
(overworked Alison)
•
•
•
•
•
First-serve apply Rule 2
Last in first serve apply rule 3
(month February)
(researching Alison)
(overworked Alison)
(marking- practicals Alison)
Recency Apply Rule that match most recent fact
Rule # 7
Fired Rules – don’t fire the same rule again
Specificity: If we had two rules but one of them
matched more facts than we’’ chose that rule
•
Prioritization If we add priority to these rules
then the higher priority rule will be fired
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

13.
Problem-solving Models
• Forward-chaining – starts from a set of
conditions and moves towards some
conclusion
• Backward-chaining – starts with a list of
goals and the works backwards to see if
there is any data that will allow it to
conclude any of these goals.
• Both problem-solving methods are built
into inference engines or inference
procedures
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

14.
Forward Chaining
• The rules are of the form:
left hand side (LHS) ==> right hand side (RHS).
• The execution cycle is
– Select a rule whose left hand side conditions match the
current state as stored in the working storage.
– Execute the right hand side of that rule, thus somehow
changing the current state.
– Repeat until there are no rules which apply.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

15.
Forward Chaining
• Facts are represented in a working memory which is
continually updated.
• Rules represent possible actions to take when
specified conditions hold on items in the working
memory.
• The conditions are usually patterns that must match
items in the working memory, while the actions
usually involve adding or deleting items from the
working memory.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

16.
Forward Chaining (example)
First we'll look at a very simple set of
rules:
1. IF (lecturing X)
AND (marking- practicals X)
THEN ADD (overworked X)
2. IF (month February)
THEN ADD (lecturing Alison)
3. IF (month February)
THEN ADD (marking- practicals
Alison)
4. IF (overworked X)
OR (slept-badly X)
THEN ADD (bad-mood X)
5. IF (bad-mood X)
THEN DELETE (happy X)
6. IF (lecturing X)
THEN DELETE (researching X)
Here we use capital letters to indicate
variables
(month February)
(happy Alison)
(researching Alison)
•
Rule 2 & 3 let’s assume rule 2 chosen
(lecturing Alison)
(month February)
(happy Alison)
(researching Alison)
•
Rule 3 & 6 apply, assume rule 3
chosen, This cycle continues and we
end up with
(bad-mood Alison)
(overworked Alison)
(marking- practicals Alison)
(lecturing Alison)
(month February)
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

17.
Example of Forward Chaining
System
•
XCON
– Developed by DEC to configures computers.
– Starts with the data about the customer order and
works forward toward a configuration based on
that data.
– Written in the OPS5 (forward chaining rule based)
language.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

18.
Backward Chaining System
If the conclusion is known (goal to be achieved) but
the path to that conclusion is not known, then
reasoning backwards is called for, and the method is
backward chaining.
• The consequence part of rule specifies combinations
of facts (goals) to be matched against Working
Memory.
• The condition part of the rule is then used as a set of
further sub-goals to be proven / satisfied.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

19.
Backward Chaining
• Start with a goal state
• System will first check if the goal matches the initial
facts given. If it does, the goal succeeds. If it doesn't,
the system will looks for rules whose conclusions
match the goal.
• One such rule will be chosen, and the system will then
try to prove any facts in the preconditions of the rule
using the same procedure, setting these as new goals to
prove.
• Needs to keep track of what goals it needs to prove its
main hypothesis.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

20.
Backward Chaining (example)
1. IF (lecturing X)
AND (marking- practicals X)
THEN (overworked X)
2. IF (month February)
THEN (lecturing Alison)
3. IF (month February)
THEN (marking- practicals
Alison)
4. IF (overworked X)
THEN (bad-mood X)
5. IF (slept-badly X)
THEN (bad-mood X)
6. IF (month February)
THEN (weather cold)
7. IF (year 1993)
THEN (economy bad )
• initial facts:
(month February)
(year 1993)
• Goal that has to be proved:
(bad-mood Alison)
• The goal is not satisfied by initial
facts.
• Rules 4 & 5 apply. Assume 4
chosen
• New Goal( overworked Alison)
• Rule 1 applies
• New Goal (lecturing Alison)
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

21.
Expert System Tools
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PROLOG
– A programming language that uses backward chaining.
ART-IM (Inference Corporation)
– Following the distribution of NASA's CLIPS, Inference Corporation implemented
a forward-chaining only derivative of ART/CLIPS called ART-IM.
ART (Inference Corporation)
– In 1984, Inference Corporation developed the Automated Reasoning Tool (ART), a
forward chaining system.
CLIPS –
– NASA took the forward chaining capabilities and syntax of ART and introduced
the "C Language Integrated Production System" (i.e., CLIPS) into the public
domain.
OPS5 (Carnegie Mellon University)
– OPS5 (Carnegie Mellon University) – First AI language used for Production
System (XCON)
Eclipse (The Haley Enterprise, Inc.)
Eclipse is the only C/C++ inference engine that supports both forward and
Backward chaining.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

22.
ES Development Life Cycles
Reformulations
Phase 1
Assessment
Requirements
Phase 2
Knowledge Acquisition
Explorations
Phase 3
Design
Refinements
Knowledge
Structure
Phase 4
Test
Evaluation
Phase 5
Documentation
Product
Phase 6
Maintenance
22
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

23.
ES Development Life Cycles
1. Assessment
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Determine feasibility & justification of the problem
Define overall goal and scope of the project
Resources requirement
Sources of knowledge
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

24.
ES Development Life Cycles
2. Knowledge Acquisition
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–
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Acquire the knowledge of the problem
Involves meetings with expert
Bottleneck in ES development
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

25.
ES Development Life Cycles
3. Design
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–
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–
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Selecting knowledge representations approach and
problem solving strategies
Defined overall structure and organization of system
knowledge
Selection of software tools
Built initial prototype
Iterative process
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

26.
ES Development Life Cycles
4. Testing
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–
–
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Continual process throughout the project
Testing and modifying system knowledge
Study the acceptability of the system by end user
Work closely with domain expert that guide the
growth of the knowledge and end user that guide in
user interface design
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

27.
ES Development Life Cycles
5. Documentation
–
Compile all the projects information into a document
for the user and developers of the system such as:
•
•
•
User manual
diagrams
Knowledge dictionary
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

28.
ES Development Life Cycles
6. Maintenance
–
Refined and update system knowledge to meet
current needs
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

29.
Some Concepts in Expert Systems
and Conventional Systems
Expert Systems
Conventional Systems Brief Comment
Inferencing
Program Flow
Inferencing is non-sequential
Knowledge Base
Database
Knowledge Base contains data
and strategies
Object Class
Relational Table
Object structure is logical not
physical
Object Instance
Relational Table Record
Represents data only, not
procedures
Object Attribute
Relational Table Field
Object attributes are inherited,
not redefined
Rule
If.. Then Statement
Rule execution is not
sequential
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

30.
Advantages of Expert
Systems
• Provide consistent answers for repetitive
decisions, processes and tasks.
• Hold and maintain significant levels of
information.
• Reduce employee training costs
• Centralize the decision making process.
• Create efficiencies and reduce the time
needed to solve problems.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

31.
Advantages (cont’d)
• Combine multiple human expert
intelligences
• Reduce the amount of human errors.
• Give strategic and comparative
advantages creating entry barriers to
competitors
• Review transactions that human experts
may overlook.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

32.
LIMITATIONS
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NARROW DOMAIN
LIMITED FOCUS
INABILITY TO LEARN
MAINTENANCE PROBLEMS
DEVELOPMENTAL COST
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

33.
Applications of Expert
Systems
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Credit granting
Information management and retrieval
Plant layout
Hospitals and medical facilities
Help desks and assistance
Employee performance evaluation
Loan analysis
Virus detection
Repair and maintenance
Shipping
Marketing
Warehouse optimization
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

34.
Applications of Expert
Systems
DENDRAL: Used to
identify the structure of
chemical compounds.
First used in 1965
LITHIAN: Gives advice
to archaeologists
examining stone tools
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

35.
Applications of Expert
Systems
MYCIN:
Medical system for
diagnosing blood disorders.
First used in 1979
DESIGN ADVISOR:
Gives advice to
designers of
processor chips
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

36.
Applications of Expert
Systems
PUFF:
Medical system
for diagnosis of
respiratory conditions
PROSPECTOR:
Used by geologists
to identify sites for
drilling or mining
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

37.
Conclusions
• Very interesting field of AI.
• Expert Systems are extremely useful in
the right domain.
• They are inflexible and require a lot of
collaboration between a knowledge
engineer and a domain expert.
• When implemented correctly, expert
systems remove human error from the
equation.
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

38.
References
• Giarratano, Riley.1994. Expert Systems: Principles
and Programming, PWS Publishing
Company, Boston. (UBE - 325)
• Gonzales & Dankel, The Engineering of Knowledge
Based Systems, Prentice Hall, 1993
• Luger, G., Stubblefield, W.A., Artificial Intelligence:
Structures and Strategies for Complex Problem
Solving, The Benjamin/Cummings Publishing
Company, inc., 1993. (UBE - 34 )
• Awad Elias M.Building Expert Systems:
Principals, Procedures and Applications 1996.
(UBE- 334)
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

39.
References
• “Expert Systems and Artificial Intelligence”.
Engelmore, R., Feigenbaum, E., Chapter 1.
http://www.wtec.org/loyola/kb/c1_s1.htm
• “The Origin of Rule-Based Systems in AI”,
Davis, R., King, J.
• “Expert System – Wikipedia”,
http://en.wikipedia.org/wiki/Expert_system
BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956