Introduction to Expert Systems (2 of 2)
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Transcript of Introduction to Expert Systems (2 of 2)
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INTRODUCTION TOEXPERT SYSTEMS
Lecture-2/2
By
Dr. M. Tahir Khaleeq
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Benefits of Expert Systems
1. Increased output and productivity
Increased outputfever workers and reduced cost
2. Increased quality
3. Reduced downtime
4. Capture of scarce expertise
5. Flexibility
6. Easier equipment operation
7. Elimination of the need for expensive equipment.
8. Operation in hazardous environments.
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9. Accessibility to knowledge and help desks
10. Reliability
11. Increased capabilities of other computerized systems.
12. Integration of several experts opinion.
13. Ability to work with incomplete or uncertain
information.
14. Provision of training.
15. Enhancement of problem solving.
16. Ability to solve complex problems.
17. Knowledge transfer to remote location.
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Problems of Expert Systems
1. Knowledge is not always readily available.
2. Expertise is hard to extract from humans.
3. It is hard, even for a highly skilled expert, to abstract
good situational assessment when he or she is under time
pressure.
4. Users of expert systems have natural cognitive limits.
5. Expert systems work well only in a narrow domain.
6. Most experts have no independent means of checking
whether their conclusions are reasonable.
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7. The vocabulary, or jargon, that experts use for
expressing facts and relations is frequently limited and
not understood by others.
8. Help is frequently required from knowledge engineers
who are rare and expensive.
9. Lack of trust by end-users may be a barrier to expert
system use.
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Types of Expert Systems
Expert Systems may be classified as following: Method of knowledge representation
Nature of system
Requirement of the system
Nature of the application.Method of Knowledge Representation
1. Rule-Based Expert Systems:
The knowledge is represented as a series of
production rules based on human expertise. Because the technology of rule-based systems is
relatively well developed, most expert systems
currently being produced are rule-based.
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Ex: Consider the likely behavior of an engineer with
a great deal of repair experience.
He looks briefly at the console. Noting the pattern of lights and error message.
Goes over to one of the cabinets, open it,
Pulls out the faulty circuit board
Insert the healthy one Restart the machine.
2. Model-Based Expert Systems:
The knowledge is represented using a model of a
system that simulates the structure and function of thesystem.
Model-based expert systems are especially useful in
diagnosing equipment problems or troubleshooting.
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Ex: Consider the new engineer fresh from training.
a great deal of repair experience.
He carefully notes the sysmptoms Gets out a thick book of schematics and spends the
next half an hour over them
At last he goes over to one of the cabinets, opens
it, Replace the faulty circuit board with the healthy
one and restart the machine.
3. Frame-Based Expert Systems:
The knowledge is represented as frames.
A representation of the object-oriented programming
approach.
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4. Hybrid Expert Systems:
These systems include several knowledge
representation approaches. Ex: Frames and Rules.
Systems Classified by their Nature:1. Evidence gathering:
Gather the evidences, which lead to the goal2. Stepwise Refinement:
It deals with the large numbers of possible outcomes bymeans of successive levels of detail.
3. Stepwise Assembly: The subject domain can have an extremely large
number of possible outcomes. Special type is called a catalog selection.
Deals with problems like choosing the thingsfrom a catalog choices.
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Requirement of the System: Expert systems can be developed to meet the particular
needs of a user, which are called Custom-made ExpertSystems.
Expert systems can be purchased as ready-made
packages for any use. The systems are called Ready-made (Turnkey) Expert Systems.
Ready-made systems are less expensive than the
customized systems.
Ready-made systems are general in nature so may not beuseful in complex situations.
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Nature of Application:
Real-time Expert Systems are systems in which there is a
strict time limit on the systems response time, which must
be fast enough for use to control the process being
computerized.
Real-time Systems always produces response by the time
it is needed.
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Rule-Based Expert Systems (RBES) In such systems the knowledge is represented as a
series of production rules based on human expertise.
RBES useful for certain classes of problems, which do
not have direct algorithmic solutions.
Rules:
Rule [rule-label]
IF logical condition statement
[AND/OR] logical condition statementsTHEN Statement conclusion
[Else] Alternate statement conclusion
Comments, may be inserted any where.
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Example: LYOPHILIZER DIAGNOSIS
Lyophilizer Fault Tree
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Rules SectionRULE 1
IF Shelf_heating_on = YESAND Faulty_heat_control = YES
THEN High_shelf_temp = TRUE;
RULE 2
IF Air_leakage = YES
OR Vacuum_pump_failure = YES
OR Faulty_pressure_control = YES
THEN Inadequate_vacuum = TRUE;
RULE 3
IF Compressor_failure = YESOR Inadequate_coolant = YES
OR Faulty_temp_control = YES
THEN `High_condenser_temp = TRUE;
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RULE 4
IF High_shelf_temp = TRUEAND Inadequate_vacuum = TRUE
THEN Excessive_heat = TRUE;
RULE 5
IF High_condenser_temp = TRUEOR Vapor_flow_throttling = YES
THEN Low_drying_rate = TRUE;
RULE 6
IF Excessive_heat = TRUE
OR Low_drying_rate = TRUE
THEN Product_melt = TRUE;
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Can be difficult to create. Can be difficult to maintain.
In large rule-bases, adding a rule can cause many
unforeseen interactions and effects => difficult to
debug. Many types of knowledge are not easily represented by
rules.
Uncertain knowledge: if it is cold it will probably
rain Information which changes over time
Procedural information (e.g. a sequence of tests to
diagnose a disease)
Limitations of Rule-Based Representations
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Where do the rules come from?
Manual Knowledge Acquisition: Interview experts
Expert describes the rules
Process of translating expert knowledge into a
formal representation.
Automated Knowledge Acquisition:
derive rules automatically from formal
specifications e.g., by automatic analysis of solution manuals
machine learning
learn rules from data
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Types of Rule-Based Systems
There are two broad kinds of rule-based systemsaccording to the search strategies:
1. Forward Chaining Systems
2. Backward chaining Systems
Forward Chaining Systems
The systems use data-driven search strategy so the
system is also called data-driven rule-based expertsystem.
The systems draw new conclusions from existing
data.
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Forward Chaining Systems (continue)
In data-driven search the problem solver begins with
the given facts of the problem and a set of rules. Searchproceeds by applying rules to facts toproduce new facts.
The process continues until it generates a path that
satisfies the goal condition.
In the system the facts are represented in a working
memory, which is continually updated. Rules in the
system represent possible actions to take when
specified conditions hold on items in the working
memory. The conditions a usually patterns that must
match items in the working memory while the actions
usually involve adding or deleting items from the
working memory.
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Backward Chaining Systems
The systems use goal-driven search strategy, so thesystem is also called goal-driven rule-based expert
system.
The expert system starts from the goal, searches
backward through successive sub-goals until it worksback to the facts of the problem.
The search is the chain of moves or rules leading from
a goal to the data.
The process is recursive, that is, a condition in a rule
may be a conclusion in another rule (other rules).
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EXAMPLE(Monitoring of a Car)
1. Two rules for the car monitoring:
Rule 1 (Forward chaining inference)
IF There is an overheatingOR The brakes respond slowly when pressed
THEN Give a message to the driver to stop the car.
Rule 2 (Backward chaining inference)
IF The temperature meter works properly
AND The temperature is over 120
THEN There is an overheating.
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Facts
Goal
Fact: Temperature = 130 Fact: Meter-OK
Inferred:overheating
Brakes respond
slowly
Inferred: stop the car
Forward Chaining Inference
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Stop the car
There is over heating
The brakesrespond slowly
The gaugeworks properly The temperatureis over 120
Goal
Yes Yes (128 )
Sub-goal
Goal
Facts
Backward Chaining Inference
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Forwards vs Backwards Chaining
The use of forwards or backwards reasoning to solve a
problem depends on the properties of the rule set and
initial facts.
If we have some particular goal ( to test some hypothesis)
then backward chaining will be much more efficient.
If we have many possible ways of trying to prove
something and we may have to try almost all of them
before we find one of that works. In this case forward
chaining is useful.
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Forwards vs Backwards Chaining (continue)
Forward chaining may be batter if we have lots of thingsto prove (or if we just want to find out in general what
new facts are true); when we have a small set of initial
facts; and when there tends to be lots of different rules
which allow us to draw the same conclusion.
Backward chaining may be better; if we are trying to
prove a single fact, given a large set of initial facts, and
where, if we used forward chaining, lots of rules would be
eligible to fire in any cycle.
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An Application
A Product of Digital Equipment Corporation XCON = rule-based expert system for computer
configuration
decides how peripherals are configured on new orders
has 10,000 rules
developed in early 1980s
based on reactive rule-based systems
if antecedents then action forward chaining in style
estimated to have saved several hundred million $s
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END
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All lectures are available on
www.geocities.com/mtkhaleeq/AI.htm