Aswini Expert Systems

7/28/2019 Aswini Expert Systems

1/37

Expert Systems

ASWINI PRAVEENA V DAS

ROLL NO:15


2/37

Content

What is an Expert System?

Characteristics of an Expert System.

Components of an Expert System.

Advantages & Disadvantages of Expert Systems.Creating an Expert System.

Applications of an expert system.


3/37

What is an ES?Expert System (ES) is a branch ofArtificial Intelligence that attempt tomimic human experts.

Expert systems can either supportdecision makers or completelyreplace them.

Expert systems are the most widelyapplied & commercially successfulAI technology.


4/37

What is an ES?

Prof. Edward Feigenbaum of Stanford University,

leading researchers in ES has produced the following

definition:

" . . . An intelligent computer program that uses

knowledge and inference procedures to solve

problems that are difficult enough to require significant

human expertise for their solution."


5/37

What is an ES?

Expertise is the extensive, task-specific knowledge acquiredfrom training, reading, and experience.

The transfer of expertise from an expert to a computer andthen to the user involves four activities:knowledge acquisition from experts or other sources.

knowledge representation in the computer.

knowledge inferencing

,resulting in a recommendation fornovices.

knowledge transfer to the user.


6/37

Expert SystemComputer software that:

Emulates human expert

Deals with small, well defined domains of expertise

Is able to solve real-world problems Is able to act as a cost-effective consultant

Can explains reasoning behind any solutions it finds

Should be able to learn from experience.


7/37

Characteristics of Expert Systems

1. High-level expertise.

The most useful characteristic of an expert system.

This expertise can represent the best thinking of

top experts in the field, leading to problem solutionsthat are imaginative, accurate, and efficient.

2. Adequate response time.

The system must also perform in a reasonable

amount of time, comparable to or better than thetime required by an expert to solve a problem.

7


8/37


3. Permits Inexact Reasoning.

These types of applications are characterized by

information that is uncertain, ambiguous, or

unavailable and by domain knowledge that isinherently inexact.

4. Good Reliability.

The system must be reliable and not prone to

crashes because it will not be used


9/37


5. Comprehensibility.

The system should be able to explain the steps of its

reasoning while executing so that it is understandable.

The systems should have an explanation capability in thesame way that human experts are suppose to be able to

explain their reasoning.

6. Flexibility.

Because of the large amount of knowledge that an expertsystem may have, it is important to have an efficient

mechanism for modifying the knowledge base.


10/37


7. Symbolic Reasoning.

Expert systems represent knowledge symbolically as

sets of symbols that stand for problems concepts.

These symbols can be combined to express relationshipbetween them. When these relationship are represented

in a program they are called symbol structures.

For example,

Assert: Ahmad has a fever

Rule: IF person has fever THEN take panadol

Conclusion: Ahmad takes panadol


11/37


8. Reasons Heuristically

Experts are adapt at drawing on their experiences tohelp them efficiently solved some current problem.

Typical heuristics used by experts:

People rarely get a cold during the summer

If I suspect cancer, then I always check the familyhistory.


12/37

Expert System

Components of an

Expert System

User

User

Interface

KnowledgeBase

Inference

Engine


13/37

Components of an

Expert System

The knowledge base is the collection of factsand rules which describe all the knowledgeabout the problem domain

The inference engine is the part of the systemthat chooses which facts and rules to applywhen trying to solve the users query

The user interface is the part of the systemwhich takes in the users query in a readableform and passes it to the inference engine. Itthen displays the results to the user.


14/37

Creating an Expert

System

Two steps involved:

1. extracting knowledge and methods from the

expert (knowledge acquisition)

2. reforming knowledge/methods into anorganised form (knowledge representation)


15/37

Acquiring the

Knowledge

What is knowledge?

Data:

Raw facts, figures, measurements

Information: Refinement and use of data to answer specific question.

Knowledge:

Refined information


16/37

Sources of Knowledge

documented

books, journals, procedures

films, databases

undocumented peoples knowledge and expertise

peoples minds, other senses


17/37

Levels of Knowledge

Shallow level:

very specific to a situation Limited by IF-THEN type rules.

Rules have little meaning. No explanation.

Deep Knowledge:

problem solving. Internal causal structure. Built from a range

of inputs

emotions, common sense, intuition

difficult to build into a system.


18/37

Categories of

Knowledge

Declarative

descriptive, facts, shallow knowledge

Procedural

way things work, tells how to make inferences

Semantic

symbols

Episodic

autobiographical, experimental

Meta-knowledge

Knowledge about the knowledge


19/37

Knowledge

Acquisition

Knowledgeacquisition is the process by which knowledge

available in the world is transformed and transferred into a

representation that can be used by an expert system. World

knowledge can come from many sources and be represented

in many forms.Knowledge acquisition is a multifaceted problem that

encompasses many of the technical problems of knowledge

engineering, the enterprise of building knowledge base

systems


20/37

Knowledge

Acquisition

Five stages:

Identification: - break problem into parts

Conceptualisation: identify concepts

Formalisation: representing knowledgeImplementation: programming

Testing: validity of knowledge


21/37

ES Development Life Cycles (ESDLC)

ESDLC contains the following phases: Assessment

Knowledge Acquisition

Design

Testing

Documentation

Maintenance


22/37

ES Development Life Cycles

Phase 1

Assessment

Phase 2

Knowledge Acquisition

Phase 3

Design

Phase 4

Test

Phase 5

Documentation

Phase 6

Maintenance

Requirements

Knowledge

Structur

e

Evaluatio

n

Product

Refinement

s

Exploration

s

Reformulation

s


23/37


1. Assessment

Determine feasibility & justification of the

problem Define overall goal and scope of the project

Resources requirement

Sources of knowledge


24/37


2. Knowledge Acquisition

Acquire the knowledge of the problem

Involves meetings with expert

Bottleneck in ES development


25/37


3. Design

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


26/37


4. Testing

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 guidethe growth of the knowledge and end user

that guide in user interface design


27/37


5. Documentation

Compile all the projects information into a

document for the user and developers ofthe system such as:

User manual

diagrams

Knowledge dictionary


28/37


6. Maintenance

Refined and update system knowledge to

meet current needs


29/37

Advantages

Capture of scarce expertise

Superior problem solving

Reliability

Work with incomplete information

Transfer of knowledge


30/37

Why use Expert

Systems?

Experts are not alwaysavailable. An expert systemcan be used anywhere, any

time.Human experts are not100% reliable or consistent

Experts may not be good atexplaining decisions

Cost effective


31/37

Limitations

Expertise hard to extract from experts

dont know how

dont want to tell

all do it differently

Knowledge not always readily

available

Difficult to independently

validate expertise


32/37

Limitations (cont)

High development costs

Only work well in narrow domains

Can not learn from experienceNot all problems are suitable


33/37

Problems with Expert Systems

Limited domain

Systems are not always up to date, and

dont learn

No common senseExperts needed to setup and maintain

system


34/37

Applications of

Expert Systems

PROSPECTOR:

Used by geologists to

identify sites for drilling or

mining

PUFF:

Medical system

for diagnosis of respiratory

conditions


35/37

Applications of

Expert Systems

DESIGN ADVISOR:

Gives advice to designers ofprocessor chips

MYCIN:

Medical system for diagnosing blood

disorders. First used in 1979


36/37

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


37/37

THANK YOU

Date post:	03-Apr-2018
Category:	Documents
Upload:	karishma-satheesh-kumar
View:	220 times
Download:	0 times

Aswini Expert Systems

Documents