expert systems

An Introduction to Expert Systems

Dr Iman Ardekani

Basics Applications Development Structure Inferencing Methods

Content

By Iman Ardekani

Iman Ardekani

What is an expert system?

By definition, an expert system is a computer program that

simulates the thought process of a human expert to solve

complex decision problems in a specific domain.

Basics

Its form?Its

Functionality?Its ideal goal?

Its responsibility?

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What is an expert system for?

For operating as an interactive system that responds to

questions, asks for clarifications, makes recommendations,

and generally aid the decision making process.

Examples:

Computer diagnosis and troubleshooting, medical diagnosis,

and even delicate medical surgery

Basics

Iman Ardekani

How an expert system operates?

It uses both facts and heuristics to solve difficult decision

problems based knowledge acquired from an expert.

Have you tried NHS expert systems? Discover how its

operates and explain it in one short paragraph.

Basics

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Why we need it?

Because of limitations associated with conventional human decision-making

processes, including:

• Humans expertise is very scarce.

• Humans get tired form physical or mental work load.

• Humans have limited working memory.

• Humans are unable to comprehend large amount of data quickly.

• Humans are slow in recalling information stored in memory.

• Humans are subject to deliberate or inadvertent in their action.

• Humans can deliberately avoid decision responsibilities.

• Humans lie, hide and die.

Basics

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Why we need it?

Because of limitation associated with conventional programming and

traditional decision-making support, including:

• They are algorithmic in nature and depend only on raw machine

power.

• They do not make use of the effective heuristic approaches used

by human expert.

• They are not easily adoptable to changing problem environment.

• They seek explicit and factual solutions that may not be

possible.

Basics

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What are main applications of expert systems?

• Interpreting and identifying

• Predicting

• Diagnosing

• Designing and planning

• Monitoring

• Debugging and testing

• Instructing and training

• Controlling

Applications

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• The best application candidates for expert systems

are those dealing with expert heuristics for solving

problems.

• Calculation-intensive applications with very

exacting requirements are better handled by

traditional decision support tools or conventional

programming.

Applications

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Hierarchy of expert systems development process

Expert System Development

Time

Task

s

Background

Concepts

Problem selection

Knowledge acquisition

Knowledge representation

Tool selection

Development

Test

Implementation and integration

Maintenance

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What is a successful expert system?

The one with making decision process similar to that of human brain.

How human brain makes a decision?

By using mixture of factual and heuristic knowledge.

What is heuristic knowledge?

It is composed of intuition, judgment and logical inference. It is an

indisputable strength of human.

Lets answer the first question more accurately now:

Successful expert systems are those combine facts and heuristics and thus

merge human knowledge with computer power in solving problems.

Expert System Development

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Expert systems are organized in 3 distinct levels:

1. Knowledge Base consists of problem solving rules,

procedures, and relevant data.

2. Working Memory refers to task-specific data for the

problem under consideration.

3. Inference Engine is a generic control mechanism

that applies the knowledge base to the task-specific

data to arrive at some solutions or conclusion.

Expert Systems Structure

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Knowledge Base

Inference Engine

Problem Data

Expertise

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Human experts or consulting firm

User

Commercial vendor e.g, VP-Expert

Knowledge Engineers Knowledge Base

Problem Data

Inference Engine

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Knowledge Base Structure

• It constitutes problem solving rules, facts or intuitions that a

human expert might use in solving problems in a given

problem domain.

• It is usually stored in terms of if-then rules.

Example

IF the patient has headache THEN ask how long he/she has

suffered this pain.


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How to create the knowledge base:

1. The knowledge engineer establishes a dialog with the

human expert to elicit knowledge.

2. The knowledge engineer codes the knowledge explicitly in

the knowledge base.

3. The expert evaluates the expert system and gives a

critique to the knowledge engineer.


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Try NHS online expert system for an imaginary

scenario. Discover the if-then rules for the chosen

scenario.


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General methods of inferencing:

1. Forward Reasoning (data-driven) – reasoning from facts to

the conclusions resulting from those facts – best for

prognosis, monitoring, and control.

2. Backward Reasoning (query driven) – reasoning in reverse

from a hypothesis, a potential conclusion to be proved to

the facts that support the hypothesis – best for diagnosis

problems.

Inferencing Methods

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Example for forward reasoning

Knowledge Base Rules:

① IF (Symptom1, X) AND (Symptom2, X) THEN ADD (Allergy, X)

② IF (Season, Spring) THEN ADD (Symptom1, Iman)

③ IF (Season, Spring) THEN ADD (Symptom2, Iman)

④ IF (Allergy, X) OR (Asthma, X) THEN ADD (Inhaler Needed, X)

⑤ IF (Inhaler Needed, X) THEN DEL (Happy X)

⑥ IF (Symptom1, X) THEN DEL (Swimming, X)

Inferencing Methods

Rule

s Pri

ori

ty

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Let us assume that initially we have a working memory (user data) with

Step 1:

Inferencing Methods

(Season, Spring)

(Happy Iman)

(Swimming, Iman)

(Symptom1, Iman) 2

3 x

(Season, Spring)

(Happy Iman)

(Swimming, Iman)

(Symptom1, Iman)

(Season, Spring)

(Happy Iman)

(Swimming, Iman)

Updated working memory:

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Step 2

Inferencing Methods

Knowledge Base Rules







(Symptom1, Iman)

(Season, Spring)

(Happy Iman)

(Swimming, Iman)

(Symptom2, Iman) 3

x6

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Step 3

Inferencing Methods

(Symptom1, Iman)

(Season, Spring)

(Happy Iman)

(Swimming, Iman)

(Symptom2, Iman)

x6

1(Allergy, Iman)








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Step 4

Inferencing Methods

(Symptom1, Iman)

(Happy Iman)

(Swimming, Iman)

(Symptom2, Iman)

4(Allergy, Iman)

Inhaler Needed, Iman

6








(Season, Spring)

x

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(Happy Iman)

Step 5

Inferencing Methods

5

6








(Season, Spring)

(Symptom1, Iman)

(Swimming, Iman)

(Symptom2, Iman)

(Allergy, Iman)


x

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(Swimming, Iman)

Step 6

Inferencing Methods








6

(Season, Spring)

(Symptom1, Iman)

(Symptom2, Iman)

(Allergy, Iman)


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Final Working Memory

Inferencing Methods

(Season, Spring)

(Symptom1, Iman)

(Symptom2, Iman)

(Allergy, Iman)


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Example for backward reasoning

Knowledge Base Rules:




④ IF (Allergy, X) THEN ADD (Inhaler Needed, X)

⑤ IF (Asthma, X) THEN ADD (Inhaler Needed, X)

⑥ IF (Season, Spring) THEN ADD (Weather, Rainy)

⑦ IF (Year, 2010) THEN ADD (Economy, Bad)

Inferencing Methods

Rule

s Pri

ori

ty

Iman Ardekani

Let us assume that initially we have a user facts set (user data) with

and we want to prove that Iman needed an Inhaler that time.

Step 1:

Inferencing Methods

(Season, Spring)

(Year, 2012)


(Allergy, Iman)

x 5

4

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Step 2: (we don’t need update the working memory)

Step 3:

(Season, Spring) is among the initial facts, so it is proven that (Inhaler

Need, Iman)

Inferencing Methods

1(Allergy, Iman)

(Symptom1, Iman)

(Symptom2, Iman)

(Symptom1, Iman)

(Symptom2, Iman) 2

3

(Season, Spring)

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Forward Reasoning vs. Backward Reasoning

Whether you use forward or backwards reasoning to solve a

problem depends on the properties of the rule set and initial facts.

Sometimes, if you have some particular goal (to test some

hypothesis), then backward reasoning will be much more

efficient, as you avoid drawing conclusions from irrelevant facts.

However, sometimes backward Reasoning can be very wasteful

- there may be many possible ways of trying to prove

something, and you may have to try almost all of them before

you find one that works.

Inferencing Methods

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Forward Reasoning may be better if you have lots of things you want to

prove, when you have a small set of initial facts; and when there tend

to be lots of different rules which allow you to draw the same

conclusion

Backward Reasoning may be better if you are trying to prove a single

fact, given a large set of initial facts, and where, if you used forward

reasoning, lots of rules would be eligible to fire in any cycle.

Inferencing Methods

The End

expert systems

Science

human expert

expert heuristics

successful expert systems

nhs expert systems

human knowledge

conventional human decision

heuristic knowledge

decision responsibilities