Fuzzy Expert Systems Lecture 2 - aShahrakiashahraki.com/Eslids/Mabahes-2.pdf · ·...

Lecture 02Lecture 02 1صفحه

Knowing ignorance is Knowing ignorance is strength; Ignoring knowledge strength; Ignoring knowledge

is sickness.is sickness.LLAOAO TTSUSU

Fuzzy Expert Systems Fuzzy Expert Systems Lecture 2Lecture 2

http://expertsys.4t.com


Fuzzy Expert Systems Lecture 2Fuzzy Expert Systems Lecture 2Review of the previous lecture:Review of the previous lecture:

Handling uncertain and incomplete data is one of the main capabilities of humans which does not exist in most conventional intelligent machines

An expert system is a computer program designed An expert system is a computer program designed to simulate the problemto simulate the problem--solving behavior of a solving behavior of a human who is an expert in a narrow domain or human who is an expert in a narrow domain or disciplinediscipline


Review of the previous lecture: Cont.Review of the previous lecture: Cont.

Expert systems are knowledge based systems which can handle the kind of uncertain and heuristic knowledge usually used by humans

Expert systems unlike other classical AI methods Implement larger amount of knowledge rather Implement larger amount of knowledge rather than sophisticated reasoning techniquesthan sophisticated reasoning techniques


Emphasis is on Knowledge not MethodsEmphasis is on Knowledge not Methods

1. Most difficult and interesting problems do not 1. Most difficult and interesting problems do not have tractable algorithmic solutionshave tractable algorithmic solutions

2. Human experts achieve outstanding 2. Human experts achieve outstanding performance because they are knowledgeableperformance because they are knowledgeable

3. Knowledge is a scarce (and therefore, valuable) 3. Knowledge is a scarce (and therefore, valuable) resourceresource

It is better to call these systems:It is better to call these systems:KnowledgeKnowledge--Based SystemsBased Systems

Review of the previous lecture: cont.Review of the previous lecture: cont.


PerishableDifficult to transferDifficult to documentUnpredictableExpensiveSlow processingCreativeAdaptiveSensory experienceBroad focus

Human Expert Vs. Expert systemHuman Expert Vs. Expert system

PermanentEasy to transferEasy to documentConsistentAffordableFast processingUninspiredNeeds to be toldSymbolic inputNarrow focus

Human Artificial Expert systemHuman Artificial Expert system


Elements of an Expert SystemElements of an Expert System


Inference Engine(The problem solving mechanism)

Reasoning, inferencing, Searching, conclusions

Scheduler (coordinates and controls rule processing)

JustifierExplains the “how” and “why” of an answer in plain English

User Interface

Knowledge Base(Domain knowledge)- FACTS- RULES

Knowledge Acquisition(interaction between knowledge engineer and domain expert)

- Rule definition

- Verification/validation of knowledge acquired

User environment

Operational environment

Developmental Environment


KnowledgeKnowledgeThe heart of an expert systemThe heart of an expert system

Knowledge is human understanding of Knowledge is human understanding of a specialized field of interest that has a specialized field of interest that has been acquired through study, and been acquired through study, and experience. It is based on learning, experience. It is based on learning, thinking and familiarity with the thinking and familiarity with the problem domain. It is not neither data problem domain. It is not neither data nor information.nor information.


Data

Information

Knowledge

From From Data Data to to KnowledgeKnowledge


Data:Data: Unprocessed facts. A static set of Unprocessed facts. A static set of elements. It does not necessarily lead to elements. It does not necessarily lead to anywhere anywhere Example: John is 6.5Example: John is 6.5--feet tall.feet tall.

Information:Information: Information is and Information is and aggregation of data that makes decision aggregation of data that makes decision making easier. It is facts or figures based making easier. It is facts or figures based on reformatted or processed data.on reformatted or processed data.Example: John Example: John ‘‘s height would make him an asset s height would make him an asset to the basketball teamto the basketball team


Knowledge: Knowledge: knowledge can be said knowledge can be said to be an understanding of information. to be an understanding of information. Knowledge includes perception, skills, Knowledge includes perception, skills, training, common sense and experience.training, common sense and experience.It is a kind of perceptive process that It is a kind of perceptive process that helps us draw meaningful conclusions.helps us draw meaningful conclusions.

Profit and loss statement exampleProfit and loss statement exampleStudent grads exampleStudent grads example


FACT:FACT: A fact is a statement of some A fact is a statement of some element of truth about a subject. element of truth about a subject. Example: Dogs need water.Example: Dogs need water.

Rule:Rule: A procedural rule describes a A procedural rule describes a sequence of relations relative to the sequence of relations relative to the domain. domain. Example:Example: IFIF dog went on long walk, dog went on long walk, THENTHEN give give dog waterdog water

Facts, Rules, HeuristicsFacts, Rules, Heuristics


Heuristic:Heuristic: A heuristic is a rule of thumb A heuristic is a rule of thumb based on years of experience.based on years of experience.Example: Give dog water after a long walkExample: Give dog water after a long walk

Example: if a person drives no more than five miles Example: if a person drives no more than five miles above the speed limit, then that person is not likely above the speed limit, then that person is not likely to be stopped for speeding.to be stopped for speeding.


Main Components of ESMain Components of ES

1. 1. Knowledge BaseKnowledge Base-- DomainDomain--specific facts and heuristics specific facts and heuristics associated with the problem. associated with the problem. Usually Usually expressed as a ruleexpressed as a rule--based system. based system.

2. 2. General Data BaseGeneral Data Base-- Relevant common knowledge, historical Relevant common knowledge, historical information, statistical data engineering information, statistical data engineering coefficients, etc.coefficients, etc.


3. 3. Inference Engine :Inference Engine :

-- A reasoning system that acts upon A reasoning system that acts upon the domainthe domain--specific knowledge, specific knowledge, general data base and problemgeneral data base and problem--specific input from the user. Inference specific input from the user. Inference engines will vary with the form of the engines will vary with the form of the knowledge baseknowledge base . .


Inference Engine:Inference Engine:1. 1. Backward/forward chaining Backward/forward chaining 2. Inheritance in frame/objects systems2. Inheritance in frame/objects systems3. Non3. Non--monotonic reasoning and truth monotonic reasoning and truth

maintenance systems maintenance systems 4. Probabilistic and Bayesian inference4. Probabilistic and Bayesian inference

5. 5. Fuzzy logic and fuzzy inferenceFuzzy logic and fuzzy inference6. 6. DempsterDempster--Shafer theory Shafer theory 7. Model logic7. Model logic


44..11 JustifierJustifier-- Explains the “how” and “why” of an answer in plain English.

An expert system (unlike a human expert) can An expert system (unlike a human expert) can explain exactly how it arrived at a solution. explain exactly how it arrived at a solution. ResultsResults11-- user trusts the system decisionsuser trusts the system decisions22-- users can learn to be experts in their own users can learn to be experts in their own rightright

Justifier and explanatory function may not be Justifier and explanatory function may not be needed in a system with high reliabilityneeded in a system with high reliability


4.2 4.2 schedulerschedulerThe main part of inference engine, the The main part of inference engine, the Scheduler Scheduler (also called rule interpreter) (also called rule interpreter) is set up to coordinate and control the is set up to coordinate and control the sequencing of the rules. The scheduler sequencing of the rules. The scheduler ensures efficient use of knowledge baseensures efficient use of knowledge base


55. . User InterfaceUser InterfaceThe user interface facilitates all The user interface facilitates all communication between the user and communication between the user and the system. the system.

11-- the system asks for information through the system asks for information through questions or multiple choice menus and questions or multiple choice menus and the user answers by typing on a keyboard the user answers by typing on a keyboard

22-- When the system completes the When the system completes the inferenceinginferenceing process, it displays its process, it displays its decision on the monitordecision on the monitor


Knowledge Knowledge The heart of expert systemThe heart of expert system

Knowledge Acquisition (KA)Knowledge Acquisition (KA)Knowledge Representation (KR)Knowledge Representation (KR)Human Computer Interaction (HCI)Human Computer Interaction (HCI)

Knowledge base Knowledge base

Inference Inference EngineEngine

KAKA KRKR HCIHCI


What is knowledge What is knowledge Acquisition?Acquisition?Knowledge acquisition is the transformation of Knowledge acquisition is the transformation of

knowledge from the forms in which it is knowledge from the forms in which it is available in the world into the forms that can available in the world into the forms that can be used by a knowledge systembe used by a knowledge system

K K*

Transferring ? NONO

Modeling ? YESYES


Transferring versus Transferring versus ModelingModelingIt is not possible to transfer directly a It is not possible to transfer directly a domain expert knowledge to a machine domain expert knowledge to a machine because the respective representations because the respective representations are to dissimilar are to dissimilar

Knowledge acquisition is a Knowledge acquisition is a modeling modeling process. A knowledge engineer builds a process. A knowledge engineer builds a theory theory of a domain and then makes that of a domain and then makes that theory theory operational.operational.


Domain ExpertDomain Expert Knowledge EngineerKnowledge Engineer User User

Expert System DevelopmentExpert System Development


The concept of The concept of SHELLSHELLAn expert system can be built using any An expert system can be built using any conventional programming language. However:conventional programming language. However:SHELLsSHELLs are special expert system development are special expert system development programs which do not need programming the programs which do not need programming the inference engine, user interface and so on.inference engine, user interface and so on.

Some statistics of using shells in the world(1992)Some statistics of using shells in the world(1992)LISP LISP 38%38% PROLOG PROLOG 33%33%Conventional Programming Conventional Programming langslangs 29%29%


Some Some SHELLsSHELLs

IBMIBM’’s expert system environment s expert system environment ESEESEVPVP--ExpertExpert $ 349$ 349EXSYSEXSYS $995$995--$2500$2500CLIPSCLIPSFuzzy CLIPSFuzzy CLIPSProlog Prolog $298$298-- $10200$10200


Development of KnowledgeDevelopment of Knowledge--Based Systems Based Systems (A historical view) : (A historical view) : Dr. Douglas Dr. Douglas DankelDankel

1965 1970 1975 1980Time Frame

DendralDendralInternistInternist

CasnetCasnetMycinMycin

HearsayHearsayPuffPuffProspectorProspector

TeiresiasTeiresiasXcon(R1)Xcon(R1)


LISPLISPSolution to Solution to complex complex symbolic symbolic problemsproblems

High level High level symbolic symbolic manipulatiomanipulation of n of algorithmic algorithmic problemsproblems

MITMITMACSYMAMACSYMA

LISPLISPDescription Description of of compoundcompound’’s s molecular molecular structurestructure

Examine Examine spectroscopispectroscopic data of all c data of all possible possible chemical chemical compoundscompounds

StanfordStanfordDENDRALDENDRAL

ToolToolExpected Expected ResultsResults

Main Main FunctionFunction

DeveloperDeveloperSystemSystem


LISPLISPPrescription for Prescription for antibiotic antibiotic treatmenttreatment

Diagnose Diagnose infectious infectious disease disease and and prescribe prescribe antibiotic antibiotic treatmenttreatment

StanfordStanfordMYCINMYCIN

SAILSAILDetailed list of Detailed list of hypotheses of hypotheses of what was what was enunciated and enunciated and corresponding corresponding best guess of best guess of its meaningits meaning

UnderstanUnderstand speech d speech

Carnegie Carnegie MellonMellon

HEARSAYHEARSAY





LISPLISPSet of Set of solutions solutions based on based on diagnosis diagnosis

Diagnose Diagnose clinical clinical diseasesdiseases

PittsburghPittsburghINTERNISTINTERNIST

LISPLISPReport for Report for attending attending physicians physicians

Diagnose Diagnose lung disease lung disease and and pulminarypulminaryproblemsproblems

StanfordStanfordPUFF PUFF

LISPLISPMap and Map and evaluation evaluation of ore of ore depositsdeposits

Evaluate Evaluate geological geological sitessites

Stanford Stanford Research Research IntInt’’l (SRI)l (SRI)

Prospector Prospector





Summary Summary “Expert Systems are a class of computerprograms that can

advise, analyze, categorize, communicate, consult, design, diagnose, explain, explore, forecast, form concepts, identify, interpret, justify, learn, manage, monitor, plan, present, retrieve, schedule, test, and tutor.

They address problems normally thought to require human specialists for their solution”

(Michaelson, Michie, & Boulanger 1985)


References of this lectureReferences of this lecture1. ES in mechanical engineering, Introduction to 1. ES in mechanical engineering, Introduction to Expert Systems, Expert Systems, Alice AgoginoAlice Agogino, , University of University of California Berkeley, 1999California Berkeley, 1999. . 2. Expert Systems, 2. Expert Systems, Massachusetts Institute of Massachusetts Institute of TechnologyTechnology http://web.mit.edu/STS001/www/Team7http://web.mit.edu/STS001/www/Team7

3. Expert Systems course, 3. Expert Systems course, Dr. Peter R. GillettDr. Peter R. Gillett, State , State University of New jersey, Rutgers, 1999.University of New jersey, Rutgers, 1999.

4. Expert Systems course, 4. Expert Systems course, Dr. Dr. ِِDouglas Douglas DankelDankel II, II, University of Florida, 2000.University of Florida, 2000.


References References (cont. )(cont. )

5. 5. Expert system metodik och verktyg, HenrikEriksson, Linköping University, Sweden

6. Building Expert Systems, 6. Building Expert Systems, principles, procedures, and principles, procedures, and applications, applications, Elias M. Elias M. AwadAwad, , west publishing co.1996west publishing co.1996

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