ch 1 fundamentals of expert systems

8/3/2019 Ch 1 Fundamentals of Expert Systems

1/48

5/5/12 Lecture One(MAH)

Expert Systems

Chapter One


2/48


Fundamentals of Expert Systems

Introduction

Expert System

Was derived fromthe term


3/48


Fundamentals of Expert SystemsHistory of Expert System

Es is developed by the AI community early mid 1960s

During this period of AI research is dominated by a believe

that few laws of reasoning coupled withpowerful computers

would produce expert or even super human performance.

Early Examples: GPS by Newell and Simon

From their logic theory of machine

Was an attempt to create an intelligent computer

Predecessor to ES

Designed to change a certain initial situation in to


4/48


Systems

History of ..(cont)

It also has an optional set ofheuristics for operators to tryfirst

In ES terms these form a rule base

GPS attempts to find list of operators that reduce thedifference between a goal and current states

Sometimes, the operators cannot operate on the current states(their preconditions are not suitable)

GPS sets itself a sub goal to change the current state into onethat is suitable for the operators

Many such sub goals may have to be set before GPS can

solve a problem


5/48


Systems

History of .(cont)

Early expert system Marked by Shift from general purpose to special purposeprograms

Mid 1960s with the development of DENDRAL by E. Feigenbaun atStanford university, followed by the development ofMYCIN and when

researchers also recognized that the problem solving mechanism is onlya small part of a complete, intelligent computer system

DENDRALconstruction led the following conclusions

GPS are too weak

Human problem solverare good only if they operate in a very narrow domain

Expert systems need to be constantly updated for new information(rule basedrepresentation is needed) and the complexity of problems requires aconsiderable amount of knowledge about the problem area

Several expert systems had begun to emerge(reading assignment)


6/48


Systems

History of .(cont)

But due to similar reasons to the general problem solvers,comprehensive knowledge had limited success

Knowledge-based problems in general werepremature

Knowledge as a target of study is too broad and diverse

Nevertheless, several different approaches to knowledge representationproved sufficient for the expert systems that employed them

Key insight learned at that time was the power of an ES that can bederived from the knowledge it possesses not from the particular

formalisms and inference scheme it employs(Expert knowledgeper seseems both necessary and sufficient to develop an expert system)

Beginning of the 1980s, ES technology, first limited to the academicscene, began to appear as commercial applications XCON (Digital

Equipment Corp.), XSEL(digital equipment corp.) and CATS (GeneralElectric


7/48


SystemsHistory of.(contd)

Programming tools (EMYCIN, AGE, EXPERT, KAS)

Tools for learning from experience(META,DENDRAL, EURISKO)

Commercially available starting in 1983

Most of the development tools required special hardware(LISP

machines)

But the late 1980s, development software can run on regular computersincluding microcomputers

Latest developments of in Expert system area

Availability ofmany tools that are designed to expedite the construction of ESat a reduced cost

Dissemination of ES in thousands of organizations, some of which has manyspecific systems

Increased use of expert system in many tasks


8/48


un amen a s o xperSystemsHistory of(contd)

Use of ES technology as a methodology for expediting the constructionof regularinformation systems

Increased use of the object-oriented programming approach inknowledge representation

Development ofcomplex systems with multiple sources of knowledge,multiple lines of reasoning and fuzzy information

Use of multiple knowledge base


9/48


Historical Overview: Detail

At the beginning(1956) all projects use the same programminglanguage such as LISP orPROLOG.

List processing Language

PROgramming in LOGic

LISP

It created in the late 1950s.

Facilitate symbol manipulation (Intelligent behaviors can berepresented easily)

It was found to be difficult to master.

So that its use is limited to AI research programs in academic

circle.

Fundamentals of ExpertSystems


10/48


Historical Overview: Detail LISP

In addition, the many dialects of LISP is a problem.

Fortunately, this situation improved in mid-1970s with the

introduction of a LISP standard called Common LISP. LISP has its roots in one area of mathematics (lambda calculus) .



11/48



PROLOG In the early 1970s PROLOG invented in France.

It also roots in one area of mathematics (first-order predicatecalculus).

PROLOG, however, did not immediately become a language ofchoice for AI programmers .

Until the early 1980s when the Japanese use a logic programminglanguage for the Fifth Generation Computing Systems (FGCS)

Project. After than researchers in the U.K. and Japan adopted PROLOG for

developing intelligent programs.

It consists of English-like statements which are facts, rules, andquestions.

Both LISP PROLOG re uired a disci lined student to master it



12/48



PROLOG Due to this until the years of1970s few expert systems were built.

Also, since these systems were built from scratch, development timewas large.

MYCIN project that dramatically changed this situation. (1976,Stanford University to aid physicians)

To diagnosing and treating patients with infectious blooddiseases caused by bacteremia .

MYCIN

Took approximately 20 person-years to complete.

MYCIN is a rule-based expert system.

That usesbackward chaining and



13/48


HistoricalOverview:Detail

MYCIN At the end of the project, the MYCIN developers realized that by

separating the knowledge on infectious diseases from its control,then the code written for the other modules should be portable toother applications.

By removing the knowledge about infectious blood diseases, asystem known as EMYCIN was formed.

EMYCIN facilitated the development of other expert systems, suchas PUFF an application for the diagnosis ofpulmonary problems.

The separation of knowledge from its processing is a powerfulfeature of expert systems that permits the reuse of existing code andgreatly reduces the development time for other systems.



14/48


Historical Overview: Detail For example, PUFF was produced in about 5 person-years.

when the number of shell vendors began to grow rapidly.

Generally the dominant languages used for building an expert systemhave been LISP, PROLOG and OPS (Official Production System ).

Recently, C and C++ have also been used for system development.Software used inexpert systemdevelopment.



15/48



OPS provides flexibility by providing many of the needed facilitiessuch as the inferenceengine and explanation facility.

The percentage of systems built using shells has increased slightly overthe 1993, while percentages for the languages have all decreased

slightly.

Number of developed expert systems peryear



16/48


SystemsBasic Concepts of Expert systems which are the core

concepts of the course The CATS-1 example introduces the basic concepts

of expert systems:

Expertise

The extensive, task-specific knowledgeacquired from training, reading, and

experience that enable experts to make better


17/48


Systems Rules(heuristics) of what to do in a given problem situation( rules regarding

problem solving)

Global strategies for solving problems Meta-knowledge (knowledge about knowledge)

It takes a long time (usually several years) to become an expert, and novices become experts only incrementally

Experts:

Difficult to define because of levels of degrees or level of expertise (how much expertise should a person

possess before qualifying as an expert)

Non experts outnumber experts in many fields

It is possible to increase top level expertise available to other decision makers

Human expertise includes a constellation of behavior that involves the following activities that must

be done efficiently (quickly and at low cost) and effectively(a high quality result)

Recognizing and formulating the problem

Solving the problem quickly and properly( ES primarily employed)

Explaining the solution (ES primarily Employed)


18/48


Systems To mimic the human expert, it is necessary to build a computer thatexhibits all the above characteristics

Transferring expertise:

the objective of an expert system is to transfer expertise from an expert

to a computer and then on to other humans(non experts).

It involves four activities:

Knowledge acquisition from experts or other sources Knowledge representation in the computer

Knowledge inferencing

Knowledge transfer to the user

The knowledge is stored in the computer in a component called aknowledge base. Two types of knowledge are distinguished: facts and

procedures(usually rules) regarding the position domain


19/48


SystemsInferencing:

A unique feature of an expert system is its ability to reason

The attempt(aim) is that all the expertise is stored in the knowledgebase and that the program has accessibility to databases, the computeris programmed so that it can make inference

The inferencing is performed in a component called the inferenceengine, which includes procedure regarding problem solving

Rules:

Most commercial ES are rule based systems; that is, the knowledge isstored mainly in the form of rules, as are the problem solving procedures.

A rule in the CATS-1 example may look like this:If the engine is idle,

and the fuel pressure is less than 38 psi, and the gauge is accurate, thenthere is a fuel system fault(60 rules of such type are there in CATS-1system

Frame representation is complementing the rule representation

n amen a s o per


20/48


un amen a s o xperSystems

Explanation capability:

Another unique feature of ES

It is expert systems ability to explain its advice or recommendationsand even to justify why a certain action was not recommended

the explanation and justification is done in a sub system called justifieror explanation system to examine its own reasoning and to explain itsoperation

The characteristics and capabilities of ES make them different fromconventional systems


21/48


SystemsConventional Systems Expert SystemsInformation and its processing are usually combined intoone sequential program

Knowledge base is clearly separated from theprocessing(inference) mechanism (knowledge rulesseparated from control)

Program does not make mistakes (programmers do)Program may make mistakes

Do not(usually) explain why input data are neededorhow conclusions are drawn

Explanation is a part of most ES

Changes in the program are tedious Changes in the rules are easy to accomplish

The system operates only when it is completed The system can operate with only a few rules

Execution is done on a step-by-step(algorithmic) basis Execution is done by using heuristics and logic

Need complete information to operate Can operate with incomplete or uncertain information

Effective manipulation of large databases Effective manipulation oflarge knowledge bases

Representation and use ofdata Representation and use of knowledge

Efficiency is a major goal Effectiveness is the major goal

Easily deal with quantitative data Easily deal with qualitative data

Capture, magnify, and distribute access to numeric dataor to information

Capture, magnify, and distribute access tojudgment anknowledge

F d t l f E t S t


22/48



Structure of Expert System

Expert systems are composed of two major parts: the development andthe consultation(runtime).

The development environment is used by ES builder to build thecomponents and to introduce a non expert to obtain expert knowledge

and advice. The following components may exist in an expert system:

Knowledge acquisition subsystem:

is the accumulation, transfer, and transformation of problemsolving expertise from some knowledge source to a computerprogram for constructing or expediting the knowledge base.

Potential sources of knowledge include human experts, textbooks, databases, special research reports, and pictures


23/48



Structure of Expert .(contd)

Knowledge acquisition systemcontd

Acquiring knowledge from experts is a complex task thatfrequently creates a bottleneck in ES construction.

The state of the art today requires a knowledge engineer tointeract with one or more human experts in building theknowledge base

Typically, the knowledge engineer helps the expert structure the

problem area by interpreting and integrating human answers toquestions, drawing, analogies, posing counter examples, and

bringing to light conceptual difficulties

Knowledge base:

Contains necessary elements for understanding, formulating and

F d t l f E i t


24/48


Fundamentals of ExperimentStructure of Expert .(contd)

Knowledge Base.contd Includes two basic elements:

Facts, such as the problem situation and theory of the problem area

Special heuristics, or rules that direct the use of knowledge to solvespecific problems in a particular domain. The heuristics express theinformal judgmental knowledge in an application area.

Global strategies , which can be both heuristics and a part of atheory of the problem area, are usually included in the

knowledge base. Knowledge, not mere facts, is the primarymaterial of expert systems.

The information in the knowledge base is incorporated in thecomputer program by a process called knowledge representation

Fun amenta Exper ment


25/48


Fun amenta Exper mentStructure of Expert .(contd)

Inference Engine

The brain of the ES is the inference engine, also known as thecontrol structure orthe rule interpreter(in rule-based ES)

This component is essentially a computer program that provides amethodology for reasoning about information in a knowledge baseand in the blackboard, and forformulating conclusions

This component provides directions about how to use the systemsknowledge by developing the agenda that organizes and controls thesteps taken to solve problems whenever consultation is performed

It has three major elements:

An interpreter(rule interpreter in most systems) , which executesthe choice agenda items by applying the corresponding knowledge

base rules)

Fundamental Experiment


26/48


Fundamental Experiment

A scheduler, which maintains control over theagenda. It estimates effects of applying inferencerules in light ofitem priorities or other criteria on theagenda

A consistency enforcer, which attempts to maintain aconsistent representation of the emerging solution

Blackboard:

Is an area of working memory set aside for the descriptionof a current problem, as specified by the input data

It is also used for recording intermediate results

It records intermediate hypotheses and decisions

Inference Engine


Fundamental experiment


27/48


Fundamental experiment

Three types of decisions can be recorded on the blackboard Plan-how to attack the problem

Agenda-potential actions awaiting execution

Solution-candidate hypotheses and alternative courses of action thatthe system has generated thus far

It exists only in some systems

User Interface:

Expert systems contain a language processor for friendly problem-oriented communication between the user and the computer.

This communication could best be carried out in natural language, and insome cases it is supplementedby menus and graphics

Black Board


Fundamentals of Expert System


28/48


Fundamentals of Expert SystemStructure of Expert .(contd)

Explanation subsystem(justifier) The ability to trace responsibility for conclusions to their

sources is crucial both in the transfer of expertise and inproblem solving

The explanation subsystem can trace such responsibilityand explain the ES behavior by interactively answeringquestions such as the following:

Why was a certain question asked by the expert system? How was a certain conclusion reached?

Why was a certain alternative rejected?

What is the plan to reach the solution? e.g. what remains to beestablished before a final diagnosis can be determined?


29/48


System


Knowledge Refining System:

Human experts have a knowledge refining system; that is, they cananalyze their own performance, learn from it, and improve it for future

consultations.

Similarly, such evaluation is necessary in computerized learning so thatthe program will be able to analyze the reasons for its success orfailure. This could lead to improvements that result in a better

knowledge base and more effective reasoning

This component is not available in commercial expert systems, butavailable in experimental expert systems in academics

E i t

Development


30/48


Environment

Blackboard(workplace)Plan Agenda

Solution ProblemDescri tion

UserInterface

Explanation

Facility

RecommendedAction

KnowledgebaseFacts what is known about

the domain areaRules Logical reference(e.g.Between symptoms and

causes)

Inference EngineInterpreterDraws Conclusions

Scheduler ConsistencyEnforcer Knowle

dgeRefine

ment

ExpertKnowled

ge

Knowledge

Engineer

ExpertKnowled

ge

Facts aboutthe specific

incident

KnowledgeAcquisiti

on

DevelopmentEnvironment

Fundamentals of Experiment System


31/48


Fundamentals of Experiment System

The human element in expert systems

At least two humans or more participate in the development and

use of an expert system At a minimum, there is an expert and a user

Frequently, there is also a knowledge engineer and a systembuilder

(Domain)Expert: A person who has the special knowledge, judgment,

experience, and methods along with the ability to apply thesetalents to give advice and solve problems

It is the domain experts job to provide knowledge about howhe or she performs the task that the knowledge system will

perform

The expert knows which facts are important and understandsthe meaning of the relationships among facts


32/48


System

The human element of expert system

Domain expert ..

The usual initial body of knowledge , including terms andbasic concepts , is documented in textbooks, reference

manuals, sets of policies, or a catalogue of products However, this is not sufficient for powerful ES

Not all expertise can be documented because most experts areunaware of the exact mental process by which they diagnoseor solve problem

Thus, an interactive procedure is needed to acquire additionalinformation from the expert to expand the basic knowledge

The process is fairly complex and usually requires the


33/48


System

The human element in expert system

The Knowledge Engineer

The knowledge engineer helps the expert(s) in structuring the problemarea by:

Interpreting and integrating to questions by

Drawing analogies posing counterexamples

Bringing to light conceptual difficulties

Knowledge engineer is also a system builder

Shortage of knowledge engineers is a major bottleneck in ESconstruction

To overcome this bottleneck ES designers are using productivitytools(special editors)

Research is being conducted on building systems that will bypass the


34/48


System

The human element in expert system

The user Most computer-based systems have evolved in a single-user mode

In contrast an ES has several possible types of users:

A non-expert client seeking direct advice to act as a consultant oradvisor

A student who want to learn so that ES can act as instructor

An ES builder who wants to improve or increase the knowledge

base so that ES can act as a partner (collaborator) An expert so that ES acts as a colleague(assistant)

The knowledge engineer and the domain expert should anticipate usersneeds and limitations when designing ES

The capabilities of ES were developed to save users time and effort


35/48


Systems

Other participants

A System builder helps to integrate the expert system withother computerized systems

A tool builder provide generic or build specific tools

Vendors that provide tools and advice Support staff provide clerical and technical help


36/48


Participants of ES

Tools,language

Vender

Expertsystem

Knowledge

engineer

Systembuilder

Expert

Documented

knowledge

Supportstaff

End user

Toolbuilder

Provide

Test

Us

e

Bui

ld

Acquir

ingKnowledge

Use Build

Build Conn

ect

Use

Sup

porttasks

Corporate

Fundamentals of expertSystems

p


37/48


FundamentalsofExpert

How expert system work

Development: construction of knowledge base(fact and

procedures), inference engine(development and

acquisition), blackboard, explanation facility, interfaces)

Consultation: Transferring to users and consulting it when

they need advice by conducting bidirectional dialoguewith the system so that they can get solutions in terms of

conclusions

Improvement of ES several times through a process called

rapid prototyping during their development

Fundamentals of Expert


38/48


Process of building (developing) ES can be lengthy.

A tool that is frequently used to expedite

development called ES shell is used

ES shell include all the generic components of an

ES but they do not include the knowledge. E.g. :

EMYCIN

Problem Areas Addressed by Expert Systems

ES systems can be classified in several ways. One

way of classifying is a generic categorization that

uses the general problem areas they address.

Fundamentals of Expertsystem

S t


39/48


Systems Generic Categories of Expert Systems

Category Problem addressed

Interpretation Inferring situation descriptions from observations (includes surveillance, speech

understanding, image analysis, signal interpretation, and many kinds of intelligenceanalysis)

Prediction Inferring likely consequences of given situations (weather forecasting, demographicpredictions, economic forecasting, traffic predictions, crop estimates, and military,marketing or financial forecasting)

Diagnosis Inferring system malfunctions from observations( medical, electronic, mechanical,and software)

Design Configuring objects under constraints (circuit layout, building design, and plantlayout)

Planning Developing plans to achieve goal(s) (routing, communications, productiondevelopment, etc)

Monitoring Comparing observations to plans(standards), flagging exceptions(air traffic, etc)

Debugging Prescribing remedies for malfunctions(

Repair Executing a plan to administer a prescribed remedy

Instruction Diagnosing, debugging, and correcting student performance

Control Interpreting, predicting, repairing, and monitoring system behaviors

S t


40/48


Tasks suitable for expert systems

To easy (Requiresconventional software)

Just right Too hard (requires humanintelligence)

Payroll, inventory Diagnosing andtroubleshooting

Designing new tools or acover for magazine

Sample tax returns Analyzing diverse data Stock market prediction

Decision trees Production scheduling Discovering new principles

Database management Equipment layout Every daylanguage(commonsense)

problems

Mortgage computation Advise on tax shelters Developing new statisticaltests

Regression analysis Determine type of

statistical analysis

Require innovation or

discovery(commonsense)

Systems


41/48


Benefits of Expert systems

Increased output and productivity: Es can work faster than

humans Increased Quality: ES can increase quality by providing consistent

advice and reducing error rate

Reduce downtime: many operational ES are used for diagnosing

malfunctions and prescribing repairs Capture of scarce resources(leave, retire, needed across a broad

geography)

flexibility in providing services and in manufacturing

Easier equipment operation: Es makes complex equipment easierto operate. E.g. STEAMER is an ES intended to traininexperienced workers to operate complex ship engines

Systems

F d t l f t S t


42/48


Fundamentals of expert Systems Benefits of .

Increased capabilities of other computerized systems(Integration) Integration several expert opinions that increase the quality of advice

Ability with to work with incomplete or uncertain information . A usercan interact with the system with do not know or not sure answer

Provision of training: ES can provide training

Enhancement of problem solving as it allows the integration of topexperts judgment into analysis. They increase users understandingthrough explanation

Ability to solve complex problems but in narrow domain

Knowledge transfer to remote locations which is more important todeveloping countries

Fundamentals of expert Systems


43/48


Benefits of .

Elimination of the need for expensiveequipment because of their ability to control

thoroughly and quickly the information

provided by instruments

Operation in hazardous environments :e.g.

in military conflicts, hot, humid, or toxicenvironments

Accessibility to knowledge and helpdesks:




44/48


Problem and limitation of expert systems

Available Es methodologies are not straight forward and effective, even

for application in generic categories. Some ES codes, especially forsystems constructed with programming languages , is generally hard tounderstand

Knowledge is not readily available

Expertise is hard to extract from humans

The approach to each expert to situation assessment may be different,yet correct

It is hard even for highly skilled professional to abstract goodsituational

Assessments when he or she is under time pressure

Users of expert systems have natural cognitive limits

ES work well only in a narrow domain, in some cases in a very narrow


un amen a s o exper ys ems


45/48


The vocabulary or jargon

that experts use for

expressing facts and

relations is frequently

limited and not understoodby others

un amen a s o exper ys ems

Representative tasks of experts and their


46/48


Representative tasks of experts and theirdifficulties

Task Difficulties

Interpretation: analysis of data to determinetheir meaning

Data are often noisy and fullof errorsData value may be missing

Diagnosis: faultfinding in a system based oninterpretation of data

Faults can be intermittentSymptoms of the faults mayinterfereData contain errors or areinaccessibleDiagnostic equipment may be

unreliable

Monitoring: continuously interpretingsignals and flag for intervention

When to flag often depends oncontextSignal expectation vary with thetime/situation

Prediction: forecasting from past andpresent

Integration of incompleteinformationAccount for multiple possiblefeaturesContingencies for uncertaintiesDiversity of data, often

contradicting data



47/48


Types of Expert Systems

Knowledge based systems can be built more easily

and quickly than expert systems. What distinguishes

expert systems from knowledge based systems is

their the amount of expertise they have.

Types

Rule-based expert systems: are mostly

commercial. The technology is relatively well

developed( E.g. MYCIN)

Frame based expert systems: Knowledge is

represented as frames, representation of the OOP




48/48

Ready made(turnkey) systems developed to satisfy a

particular needs of a users(custom made) or they canbe purchased as ready-made packages for any users.

Ready made systems are similar to application

packages. They are considered as less expensive

than customized systems.

But they are general in nature and their value maynot important to the user. are not popular.

Real-time expert systems: systems in which there is


ch 1 fundamentals of expert systems

Documents