Introduction to Expert Systems (2 of 2)

8/7/2019 Introduction to Expert Systems (2 of 2)

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3/16/2011 Total Slides 27 1

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

Introduction to Expert Systems (2 of 2)

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