introduction to expert systems (1 of 2)

8/7/2019 Introduction to Expert Systems (1 of 2)

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INTRODUCTION TOEXPERT SYSTEMS

Lecture-1/2

By

Dr. M. Tahir Khaleeq


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Expert Systems

Expert Systems are knowledge-based systems whichcontain expert knowledge and can provide an expertise,

similar to the one provided by an expert in a restricted

application area.


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An expert system is a program that can provide expertise

for solving problems in a defined application area in the

way the expert do.

The two sides of an expert system:

1. The experts side

2. The users side

It takes a long time (usually several years) to become an

expert.


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Example:

Expert system for diagnosis of cars has a knowledge base

containing rules for checking a car and finding faulty

elements, as it would be done by a specialized engineer.

IF (meter is OK)

AND [TEMPERATURE] > 120

THEN Cooling system is in the state of overheating


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Expert Systems? Why

Order-of-magnitude increase by expert in solving tasks

Increased quality of work

Reduced errors Reduced cost

Decreased personnel and training time

Improved decisions

Improved customer service


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Expertise

Expertise is the extensive, task-specific knowledgeacquired from training, reading, and experience.

Expertise includes:

- Facts about the problem area.- Theories about the problem area.

- Hard-and-fast rules and procedures regarding the

general problem area.

- Rules(heuristics) of what to do in a given problemsituation (rules regarding problem solving).

- Global strategies for solving these type of problems.

- Meta - knowledge (knowledge about knowledge)


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Facts about Expertise

Experitse is usually associated with

High degree of intelligence Quality of knowledge

Experts

Experts behave the following activities: Recognizing and formulating the problem

Solving the problem quickly and properly

Explain the solution

Learning from experience (past successes and mistakesRestructuring knowledge

Determining relevance

Awareness of limitations.


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Expert Systems Facilities

Representing existing expert knowledge

Accommodating existing database

Learning and accumulating knowledge during

operation Learning new pieces of knowledge from existing

database

Making logical inferences Making decision and giving recommendations

Communicating with user in a friendly way.


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Expert systems have been used successfully in about every

field of human activity, including Engineering, Science,

Medicine, Manufacturing, Education and Training,

Business, Finance and design.


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Expert System Shells

Previous Expert systems were all written in Lisp, C,Prolog, etc...

In order to speedup the design process, expert system

shells are developed. They have architecture of an expert

system, but are empty of knowledge. The knowledge isentered in the empty knowledge-based modules of the

shell.

By using shells it becomes much easier to build an

Expert System.

Examples: EMYCIN, OPS5 (rule-based), SOAR

(revision of OPS5 with chunking), KADS (Knowledge

Acquisition), CSRL, DSPL, RA, Peirce (GT tools)


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Expert System Architecture

Knowledge

Base

Data

Base

Inference

Engine

Knowledge

Acquisition

User

InterfaceExplanation

Experts


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The Knowledge Base Module

It is where the problem knowledge residesIt may be a production memory in production

languages.

It may be a set of fuzzy rules in a fuzzy system

It may be neural networks (has been trainind withthe past data) in the connectionist expert systems.

The Database Module

It is the working memory in the production languages. In some architectures this module can be used as a source

of knowledge (past data) in addition to the knowledge

base.


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The Inference Engine

It is a program that controls the functioning of the expert

system. It contains an inference mechanisms, either forward

chaining, or backward chaining, or a combination of

them.

The Explanation Module

It is a module that traces the execution of the expert

system and transfers this accumulated information to theuser.

EX: WHY it is checking a condition element in a rule, or

HOW it has inferred some conclusion.


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Stop the car

There is over heating

The brakes respond slowly

The meter works properly The temperature is over 120

How?

Why?

How?Why?


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The User Interface Module

Its role is to communicate with the environment, tointeract with the user in a friendly way.

Natural language and speech processing may be used

for communication with users.

The Knowledge Acquisition Module

It is designed to accumulate knowledge to build up the

knowledge base.

Knowledge Acquisition: Interview experts Learning from data Literature

Agents on the Web


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An expert systems design is very much a heuristic processalthough some global phases have been shared among

expert system designers.

Five Stages of Expert System Development

IDENTIFICATION CONCEPTUALIZATION FORMULATION

REALIZATIONVALIDATION

Expert System Design


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Stage-1 Identification of the Problem

What class of problems with the expert system be

expected to solve?

How can be these problem be defined?

Stage-2 Conceptualization

What is given and what should be inferred?

What types of data and knowledge are available?

Is there a need for knowledge acquisition?


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Stage-3 Formulization

Here major issues related to the knowledge should be

discussed. The issues are:1. Representation 2. Inference 3. Learning

4. Generalization 5. Interaction 6. Explanation

7. Validation 8. Adaptation.

Are data and knowledge are insufficient, or plentifuland redundant?

Is there a need to deal with uncertainty?

Are data and knowledge reliable, accurate and precise,

or unreliable, inaccurate, and imprecise?Are they consistent and complete?

What kind of explanation is needed/


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Stage-4 Realization

What methods and tools are appropriate for

representation data and knowledge: data basedsystems, symbolic AI methods, fuzzy systems, neural

networks etc?

Extendibility, friendliness, reliability, robustness of

the realization.Stage-5 Experiments and Validation of the Results

How to evaluate the system and the error?

How to validate the results:

Compare experimental results with the resultsobtained by experts,

Compare results with the results obtained by other

methods etc.


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The Iterative Process of Identifying the Problem

Does

Description Adequately

Describe

Problem?

Expert Describes CasesKnowledge Engineer Writes

Problem Description

Description Complete

NO

YES

Process

begins


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The Iterative relationship between

the Identification and Conceptualization

Is

Identification

Adequate ?

Identification Conceptualization

Development Process Continues

NO

YES

Process

begins


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1. How to acquire knowledge from experts?

2. How to extract knowledge from a huge mass of

previously collected data?

3. How to represent incomplete, corrupted and

contradictory data and knowledge?

4. How to perform approximate reasoning?

Fuzzy systems and neural networks provide solutions to

these problems.

Main Problems in Building

Expert Systems


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Categories of Expert Systems

Interpretation SystemsInferring situation descriptions from observations.

Ex: Surveillance, Speech understanding, Image analysis,

Signal interpretation, and many kinds of intelligence

analysis.

Prediction Systems

Inferring likely sequences of given situations.

Ex: Weather forcasting, Demographic predictions,Economic forcasting, Traffic predictions, Crops

estimates, and Military, Marketing, or Financial

forecasting.


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Diagnostic Systems

Inferring system malfunctions from observations.

Ex: Medical, Electronic, Mechanical, Software.

Design Systems

Configuring objects under constraints.

Ex: Circuit layout, Building design, Plant layout.

Planning Systems

Develop plans to achieve goals.

Ex: Project management, Routing, Communications,Product development, Military applications and

Financial planning.


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Monitoring Systems

Compare observations of system behavior with standards.

Ex: Plant monitoring, Aircraft monitoring.Debugging Systems

Prescribing remedies for malfunctions.

Repair Systems

Develop and execute plans to administer a remedy forsome diagnosed problems. Such systems incorporate

debugging, planning and execution capabilities.

Instruction Systems

Diagnosing, debugging, and correcting studentperformance.

Control Systems

Adaptively govern the overall behavior of a system.


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Examples of Expert Systems

1965 DENDRAL Stanford analyze mass spectrometry data1965 MACSYMA MIT symbolic mathematics problems

1972 MYCIN Stanford diagnosis of blood diseases

1972 Prospector SRI Mineral Exploration

1975 Cadeceus U. of PITT Internal Medicine

1978 DIGITALISMIT digitalis therapy advise

1979 PUFF Stanford obstructive airway diseases

1980 R1 CMU computer configuration

1982 XCON DEC computer configuration

1983 KNOBS MITRE mission planning

1983 ACE AT&T diagnosis faults in telephone cables

1984 FAITH JPL spacecraft diagnosis

1986 ACES Aerospace satellite anomaly diagnosis

1986 Cambpell diagnose cooker malfunctions

1986 DELTA/CATS GE diagnosis of diesel locomotives

1987 AMEX credit authorization

1992 MAX NYNEX telephone network troubleshooting

1995 Caltech PacBell network management

1997 UCI planning drug treatment for HIV


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END

introduction to expert systems (1 of 2)

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