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CSNB234CSNB234ARTIFICIAL INTELLIGENCEARTIFICIAL INTELLIGENCE

Chapter 7Introduction to Expert Systems

Chapter 7Introduction to Expert Systems

Instructor: Alicia Tang Y. C.

(Chapter 8, Textbook)(Chapter 3 & Chapter 6, Ref. #1)

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EXPERT SYSTEM (ES)EXPERT SYSTEM (ES)

Definition– ES is a set of computer programs

that can advise, consult, diagnose, explain, forecast, interpret, justify, learn, plan and many more tasks that require ‘intelligence’ to perform.

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An “expert system” is defined as

““A computerized clone of a human expert””

(Definition taken from Oxford Science Publication)

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EXPERT SYSTEMS: CHARACTERISTICSCHARACTERISTICS

– Perform at a level equivalent to that of a human expert.

– Highly domain specific.– Adequate response time– Can explain its reasoning.– It can propagate uncertainties and provide

alternate solutions through probabilistic reasoning or fuzzy rules .

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AN EXPERT AND A SHELL

EXPERT: An expert in a

particular field is a person who possess considerable knowledge of his area of expertise

ES SHELL A special purpose tool

designed for certain types of applications in which user supply only the knowledge base (e.g. EMYCIN)

It isolates knowledge-bases from reasoning engine

Hence software portability can be improved

Domain-specific

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Shell Concept for Building Expert Systems

KBe.g. rules

Consultation Manager

KB Editors& debugger

ExplanationProgram

KBMFInference Engine

shell

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ComparisonComparison (I)(I) Conventional Systems

– information & its processing are combined in one sequential program

– programs do not make mistake (but programmers do make it)

– the system operates only when it is completed

– execution is done on a step-by-step basis (i.e. it is algorithmic)

Expert Systems– knowledge base is separated

from the processing (inference) mechanism

– program may make mistake (we want it to make mistake!)

– explanation is part of most ES– the system can operate with

only a few rules (this is called fast prototyping)

– changes in the rules are easy to accomplish

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ComparisonComparison (II)(II) Conventional Systems

– changes in programs are tedious

– do not usually explain why or how conclusions were drawn

– need complete information to operate

– E__________ is a major goal

– easily deal with q_________ data

Expert Systems– can operate with

incomplete or uncertain information

– execution is done by using heuristic based on rules of logic

– E___________ is the major goal

– easily deal with q______ data

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RIGHT TASKS FOR RIGHT SYSTEMS

Facts that are knownExpertise available but is expensive

Analyzing large/diverse data– E.g. Production scheduling & planning, diagnosing and troubleshooting, etc.

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Generic Categories of Expert Systems (1)

Interpretation– inferring situation descriptions from

observationPrediction

– inferring likely consequences of given situations

Diagnosis– inferring system malfunctions from

observations

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Generic Categories of Expert Systems (2)

Design– configuring objects under constraints

Planning– developing plans to achieve goals

Repair– executing a plan to administer a

prescribed remedy

Other categories include: monitoring, debugging, control, instruction

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BENEFITS OF EXPERT SYSTEMS (I)

Expertise in a field is made available to many more people (even when human experts are not around in the company).

Top experts’ knowledge gets saved rather than being lost, when they retire or should they have resigned.

Facts are stored in a “Systematic” way. & Easy to keep on adding new knowledge on it

Allows human experts to handle more complex problems rapidly and reliably.

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Early EXPERT SYSTEMS (70s – mid 80s) MYCIN

– USES RULE-BASED SYSTEM– GOAL-DRIVEN– RULES INCORPORATED IN MYCIN REFLECTED UNCERTAINTY ASSOCIATED WITH KNOWLEDGE

– CERTAINTY FACTOR WAS USED TO DERIVE CONCLUSION DENTRAL

– WAS DEVELOPED IN STANFORD UNIVERSITY TO ANALYZE AND INTERPRET CHEMICALS AND THEIR MOLECULAR STRUCTURES

– DEVELOPERS INCLUDE JOSHUA LEDERBERG (NOBEL PROZE WINNER IN GENETICS)

– EXPERT’S “KNOW-HOW” ARE EXPRESSED IN RULES; RULE-OF-THUMB TECHNIQUE IS USED

PROSPECTOR– KBS TO INTERPRET GEOLOGIC DATA FOR MINERALS EXPLORATION

– INCOPORATED BAYES THEOREM (PROBABILISTIC REASONING APPROACH)

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Early EXPERT SYSTEMS (70s – mid 80s)

XCON–RULE-BASED SYSTEM, DATA-DRIVEN

REVEAL–FUZZY LOGIC USED

CENTAUR–RULES AND FRAMES-BASED SYSTEM

HEARSAY I – for speech recognition

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Characteristics common to Characteristics common to early ESearly ES

Could perform at a level equivalent to human experts

Large amount of domain specific knowledge

Rule-based systems: knowledge incorporated in the form of production rule

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Popular Expert Systems Application Domain

Electronics : helps in VLSI/ULSI design

Law : system serves as an auditor

Manufacturing : in production & process controls

Medicine : illness diagnosis

Chemistry : synthesis planning

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EXPERT SYSTEMS:LIMITATIONS

SYSTEMS ARE TOO SUPERFICIALRAPID DEGRADATION OF PERFORMANCEINTERFACES ARE STILL CRUDEINABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING– E.g. either forward or backward and not both

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The ResistorsThe Resistors

Domain expertsNon-expertsOther information technologists such as DBA, Network specialists

UsersManagementTroublemakers

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Consultation Environment(Use)

Development Environment(Knowledge Acquisition)

User Expert

User Interface

Inference Engine

ExplanationFacility

Working Memory

Facts ofthe Case

Recommendation,Explanation

Facts ofthe Case

KnowledgeEngineer

KnowledgeAcquisition

Facility

KnowledgeBase

Domain Knowledge(Elements of Knowledge Base)

STRUCTURE OF AN EXPERT SYSTEM

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Figure: Key components of an Expert Systems

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Explanation FacilityExplanation Facility

Why need it?– It provides sound reasoning besides quality result.

Common types– “How” a conclusion was reached– “Why” a particular question was asked

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Importance of ExplanationImportance of Explanation

It can influence the ultimate acceptance of an Expert System.

Use as a debugging tool.Use as a component of a tutoring system.

Who needs explanation?Our clients : to be convinced to

purchase.Knowledge Engineer: to check if all

specifications are met?

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Approaches Used (1)Approaches Used (1)

Canned Text– prepared in advance all questions and

answers as text– system finds explanation module and

displays the corresponding answer– problem:

difficult to secure consistency

– suitable for slow changing system only

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Paraphrase– Tree Traverse

to answer WHY– look up the tree

to answer HOW– look down the tree to see sub goals

that were satisfied to achieve the goal

Approaches Used (2)Approaches Used (2)

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Rule-based Systems

In expert system development, a tool is used to help us to make a task easier. The tool for machine thinking is the

Inference Engine.

Most expert systems are rule-based.

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FACTS AND RULES (revision)FACTS AND RULES (revision)

FACTS : A mammal is an animal A bird is an animal Arthur is a man Ben drives a car Catherine has blue eyes

RULES : If a person has RM1,000,000 then he is a

millionaire. If an animal builds a nest and lays eggs then

the animal is a bird.

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Rule 1: if you work hard and smartthen you will pass all examinations

Rule 2: if the food is goodthen give tips to the waiter

Rule 3: if a person has US1,000,000then he is a millionaire

Examples of rules:

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These are methods for deducing conclusions. The former predicts the

outcome (conclusion) from various factors (conditions) while the latter could be very useful in trying to determine the causes

once something has occurred.

Detailed description and working examples of rule-based systems and their

reasoning methods will be dealt separately in other chapters.

Forward Chaining and Backward Chaining

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Chaining SystemsChaining Systems

Forward– it predicts the

outcome from various factors (conditions)

Backward– it could be very

useful in trying to determine the cause (reason) once something has occurred

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InputData

Few Items(For Example, UserSpecifications fora Computer System)

Conclusion(Goals)

Many Possibilities(For Example, a ComputerConfiguration)

(a) Forward Chaining: IF - Part Matches Shown

Inference Strategies (I)

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InputData

Extensive;Much of the DataObtained by theSystem Queryingthe User (ForExample,Investor’s Profile)

Conclusion(Goals)

Few Possibilities(Known in Advance((For Example, Investment Options)

(b) Backward Chaining: THEN - Part Matches Shown

Inference Strategies (II)

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Exercise #1Exercise #1

You have seen what tasks are “just right” for ES and now you are

required to answer the following question:

– List a “Too hard” task for computers and explain briefly why they are considered ‘too

difficult’.

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Nice to know…Nice to know…

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RULE-BASED VALIDATION

There are essentially 5 types of inconsistency that may be identified, these are:– Redundant rules– Conflicting rule– Subsumed– Unnecessary Premise(IF) Clauses– Circular rules

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REDUNDANT RULES

Rule 1– IFA = X AND B= Y THEN C = Z

Rule 2– IF B=Y AND A=X THEN C=Z AND

D=WRule 1 is made redundant by rule 2.

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CONFLICTING RULES

Rule 1

–IF A = X AND B= Y THEN C = Z Rule 2

–IF A=X AND B=Y THEN C=W Rule 1 is subsumed by rule 2 thus

becomes unnecessary.

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SUBSUMED RULES

Rule 1

–if A = X AND B= Y THEN C = Z Rule 2

–if A=X THEN C=Z to be revised.

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UNNECESSARY PREMISE (IF) CLAUSES

Rule 1

–IF A = X AND B= Y THEN C = Z Rule 2

– IF A=X AND NOT B=Y THEN C=Z Remove B=Y and NOT B=Y to

have just one rule.

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CIRCULAR RULES

Rule 1– IF A = X THEN B = Y

Rule 2– IF B=Y AND C=Z THEN DECISION=YES

Rule 3 IF DECISION=YES THEN A = X

Restructure these rules !