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    Topic: Fuzzy LogicKnowledge Acquisition andInterface Design

    Group 3:Rahul Sharma

    Sumar Loomba

    Nisheeth Gupta

    Pallavi SagneRitu Khushwaha

    Prashanth R

    Praveen Rathod

    Rohan Dange

    Rakesh Kumar 1

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    FUZZY LOGIC

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    WHAT IS FUZZY LOGIC?

    Definition of fuzzy

    Fuzzynot clear, distinct, or precise; blurred

    Definition of fuzzy logic

    A form of knowledge representation suitable for notions that

    cannot be defined precisely, but which depend upon their

    contexts.

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    TRADITIONAL REPRESENTATION OF

    LOGIC

    Slow Fast

    Speed = 0 Speed = 1bool speed;

    get the speed

    if ( speed == 0) {

    // speed is slow}

    else {

    // speed is fast

    }

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    FUZZY LOGIC REPRESENTATION

    For every problem

    must represent in terms

    of fuzzy sets.

    What are fuzzy sets?

    Slowest

    Fastest

    Slow

    Fast

    [ 0.00.25

    ]

    [ 0.250.50 ]

    [ 0.500.75 ]

    [ 0.751.00 ]

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    FUZZY LOGIC REPRESENTATION CONT.

    Slowest Fastestfloat speed;

    get the speed

    if ((speed >= 0.0)&&(speed < 0.25)) {

    // speed is slowest

    }

    else if ((speed >= 0.25)&&(speed < 0.5))

    {

    // speed is slow

    }else if ((speed >= 0.5)&&(speed < 0.75))

    {

    // speed is fast

    }

    else // speed >= 0.75 && speed < 1.0

    {

    // speed is fastest

    }

    Slow Fast

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    ORIGINS OF FUZZY LOGIC

    Traces back to Ancient Greece

    Lotfi Asker Zadeh ( 1965 )

    First to publish ideas of fuzzy logic.

    Professor Toshire Terano ( 1972 )

    Organized the world's first working group on fuzzy systems.

    F.L. Smidth & Co. ( 1980 ) First to market fuzzy expert systems.

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    FUZZY LOGIC IN CONTROL SYSTEMS

    Fuzzy Logic provides a more efficient and resourceful

    way to solve Control Systems.

    Some Examples

    Temperature Controller

    AntiLock Break System ( ABS )

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    TEMPERATURE CONTROLLER

    The problem

    Change the speed of a heater fan, based off the room

    temperature and humidity.

    A temperature control system has four settings

    Cold, Cool, Warm, and Hot

    Humidity can be defined by:

    Low, Medium, and High

    Using this we can define

    the fuzzy set.

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    BENEFITS OF USING FUZZY LOGIC

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    ANTI LOCK BREAK SYSTEM ( ABS )

    Nonlinear and dynamic in nature

    Inputs for Intel Fuzzy ABS are derived from

    Brake

    4 WD

    Feedback

    Wheel speed

    Ignition

    Outputs

    Pulsewidth

    Error lamp

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    FUZZY LOGIC IN OTHER FIELDS

    Business

    Hybrid Modeling

    Expert Systems

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    Fuzzy Logic and Knowledge

    Based Systems (AI)

    Knowledge Acquisition

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    What is Knowledge Acquisition?

    Knowledge acquisition(KA) is the process of acquiringknowledge from a human expert for an expert system whichmust be carefully organized into if-then else rules or some otherform of knowledge representation. KA is the process of

    absorbing and storing new information in memory, the successof which depends on how well the information can later beretrieved from memory. The process of storing and retrievinginformation depends heavily on the representation andorganization of the information.

    27/02/2013 14

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    Knowledge Acquisition INTRODUCTION& BACKGROUND

    The important characteristics of knowledgeare that it isexperiential, descriptive, qualitative, largely undocumentedandconstantly changing.

    There are certain domains where all these properties are found

    and some where there are only a few.

    The lack of documentation and the fact that experts carry a lot ofinformation in their heads, make it difficult to gain access to theirknowledge for developing information systems in general and

    expert systems in particular.

    Therefore, knowledge engineers have devised specialisedtechniques to extract and document this information in an efficient

    and expedient manner: Knowledge Acquisition.

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    Knowledge AcquisitionINTRODUCTION& BACKGROUND

    Currently knowledge bases for knowledge based systems are crafted

    by hand, this is a severe limitation on the rapid deployment of such

    systems.

    The automation of knowledge acquisition (from text) would greatly ease

    this problem.

    There is considerable interest in developing software tools which would

    allow the automatic construction of knowledge bases from textualinformation.

    This will provide the opportunity to rapidly build knowledge bases thus

    increasing, for example, the rate at which knowledge based systems can

    be developed and deployed

    Knowledge acquisition can be regarded as a method by which aknowledge engineer obtains information from experts, text books,

    and other authoritative sources for ultimate translation into a

    machine language and knowledge base.

    The person undertaking the knowledge acquisition must convert the

    acquired knowledge into a form that a computer program can use.

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    Knowledge AcquisitionINTRODUCTION& BACKGROUND

    In the process of Knowledge Acquisition for an Expert System Project,

    the knowledge engineer basically performs four major tasks in

    sequence:

    First, the engineer ensures that he or she understands the aims and objectives of

    the proposed expert system to get a feeling for the potential scope of the

    project.Second, he or she develops a working knowledge of the problem domain by

    mastering it's terminology by looking up technical dictionaries and

    terminology data bases. For this task the key sources of knowledge are

    identified: textbooks, papers, technical reports, manuals, codes of practice,

    and domain experts.

    Third, the knowledge engineer interacts with experts via meetings or interviewsto acquire, verify and validate their knowledge.

    Fourth, the knowledge engineer produces a "paper knowledge base"; a document

    or group of documents which form an intermediate stage in the translation of

    knowledge from source to computer program. This comprises the interview

    transcripts, the analysis of the information they contain and a full descriptionof the major domain entities (e.g. tasks, rules and objects).

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    Knowledge AcquisitionINTRODUCTION& BACKGROUND

    Knowledge engineers interview experts in a specialist domain about how

    he or she solves a given problem. Before interviewing the experts, the

    knowledge engineers have to formulate their questions, and after the

    interview the answers to the questions have to be analyzed.

    The knowledge engineer has to familiarize himself or herself with the

    terminology of the specialist domain; he or she has to consult technical

    manuals, and in some cases learned papers, to see how the experts knowledge

    is applied; the knowledge engineers sometimes consults textbooks or

    encyclopedic texts for understanding the conceptual structure of the experts

    domain.

    In many different ways the knowledge engineer literally has to come to

    terms with the language used by the expert and that used in the other

    texts mentioned above. The knowledge engineer should become

    conversant in the specialist language of his or her application domain.

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    Knowledge acquisition involveselicitation, analysis, modelling andvalidation of knowledge

    1. Employing a technique to elicit data (usually verbal) from the expert.

    2. Interpreting these verbal data (more or less skilfully) in order to inferwhat might be the expert's underlying knowledge and reasoning

    process.

    3. Using this interpretation to guide the construction of some model orlanguage that describes (more or less accurately) the expert'sknowledge and performance.

    4. Interpretation of further data is guided in turn by this evolvingmodel.

    5. The principle focus for the knowledge acquisition team should be inconstructing models, in domain definition, or problem identificationand problem analysis.

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    Roles for knowledge acquisition

    Knowledge engineering and management: technological

    innovation, ontology construction, document mark-up

    AI systems development: generic methodologies (e.g., KADS: KADSstands for ``Knowledge Analysis and Documentation System''. Later on,other interpretations have been given to this acronym, such as``Knowledge Analysis and Design Support'. KADS is the name of a

    structured methodology for the development of knowledge based systemsthat is now in practical use in many places in Europe and elsewhere.)

    Organizational analysis:process approaches

    Task analysis:job design

    User analysis: generation of cognitive specifications for tasks, themitigation of human error in domains of risk or time pressure, theenhancement of proficiency through training and skill remediation

    Requirements elicitation: systems or design analysis, conceptualdatabase design, software requirements definition

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    Preliminary Work I involves Reading,Observation, Discussion

    Preliminary work is carried out by knowledge engineer(s)

    Knowledge engineering is knowledge acquisition for expert systemdevelopment, and used to describe the reduction of a large body ofknowledge to a precise set of facts and rules

    Knowledge engineer is a computer software programmer who gathersknowledge from experts and then translates the knowledge into the

    knowledge base of a computerised expert system in a structured and logicalway, and eventually constructs computerised expert systems.

    "Knowledge acquisition is a bottleneck in the construction of expertsystems.The knowledge engineer's job is to act as a go-between to help anexpert build a system. Since the knowledge engineer has far less knowledge

    of the domain than the expert, however, communication problems impedethe process of transferring expertise into a program. The vocabularyinitially used by the expert to talk about the domain with a novice is ofteninadequate for problem-solving; thus the knowledge engineer and expertmust work together to extend and refine it. One of the most difficult aspectsof the knowledge engineer's task is helping the expert to structure the

    domain knowledge, to identify and formalize the domain concepts."(Ref:

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    Preliminary Work - II

    When acquiring knowledge about a domain it is absolutely crucial that the

    knowledge engineer can converse with the expert using the expertterminology.

    The knowledge engineer has to have a good grasp of the domain to be ableto ask intelligent questions to extract important and relevant knowledgefrom the experts who have vast amounts of knowledge a lot of which istacit knowledge.

    The knowledge engineer must therefore do some preliminary workincluding research on the domain in question before the first interview withthe expert takes place.

    Some requirements for KA Techniques

    Take experts off the job for short time periods

    Allow non-experts to understand the knowledge Focus on the essential knowledge

    Try to capture tacit knowledge

    Allow knowledge to be collated from different experts

    Allow knowledge to be validated and maintained

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    The Knowledge Handbook

    One of the functions of the knowledge engineer during the knowledgeacquisition phase is to document the knowledge that has been acquired. One

    idea suggested (Wolfgram et. al. 1987 and others) is that of building aknowledge handbook.

    Wolfgram et. al. describe the contents of the knowledge handbook as follows:

    The general problem description.

    Who the users are and their expectations from the system.

    A breakdown of the problems into sub-problems and sub-domains forfuture knowledge acquisition.

    A detailed description of the domain or sub-domain to be used for the

    prototype.

    A bibliography of reference documents.

    A list of vocabulary, concepts, terms, phrases and acronyms in the domain.

    A list of experts for the prototype.

    Some reasonable performance standards for the system, based on

    consultation with the experts and users.

    Descriptions of typical reasoning scenarios gained from the knowledgeacquisition.

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    Basic knowledge engineering forknowledge acquisition - I

    Knowledge engineer act as a go-between the expert and knowledge base.This can be achieved by means of eliciting knowledge from the expert,encoding it for the knowledge base, and refining it in collaboration with theexpert in order to achieve acceptable performance. The process is basicallyas follows:

    The knowledge engineer interviews the expert to elicit his or herknowledge;

    The knowledge engineer encodes the elicited knowledge for theknowledge base;

    The shell uses the knowledge base to make inferences about particularcases specified by clients;

    The clients use the shell's inferences to obtain advice about particularcases

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    Basic knowledge engineering forknowledge acquisition - II

    Basic knowledge engineering model with manual acquisition of knowledgefrom an expert (left-hand side of the figure). This is also followed byinteractive application of the knowledge with multiple clients through anexpert system shell (right-hand side of the figure).

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    Interactive Knowledge Engineering forInteractive Knowledge Acquisition -I

    In an interactive knowledge engineering process for interactive knowledge

    acquisition, knowledge engineers have responsibility for: Advising the experts on the process of interactive knowledge

    elicitation;

    Managing the interactive knowledge acquisition tools, setting them upappropriately;

    Editing the uuencoded knowledge base in collaboration with theexperts;

    Managing the knowledge encoding tools, setting them upappropriately;

    Editing the encoded knowledge base in collaboration with the experts;

    Validating the application of the knowledge base in collaboration with

    the experts; Setting up the user interface in collaboration with the experts and

    clients;

    Training the clients in the effective use of the knowledge base incollaboration with the expert by developing operational and training

    procedures.

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    Interactive Knowledge Engineering forInteractive knowledge acquisition -II

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    Interactive Knowledge Engineering forInteractive Knowledge Acquisition -IIIInteractive knowledge acquisition and encoding tools can greatly reduce the

    need for the knowledge engineer to act as an intermediary but, in mostapplications, they leave a substantial role for the knowledge engineer.

    This use of interactive elicitation can be combined with manual elicitation and

    with the use of the interactive tools by knowledge engineers rather than, or in

    addition to, experts. Knowledge engineers can directly elicit knowledge from

    the expert and use the interactive elicitation tools to enter knowledge into the

    knowledge base.

    Such approach is very useful and effective as it allows use of

    Multiple knowledge engineers since the tasks may require the effort of

    more than one person, and some specialization may be appropriate

    Multiple experts since one person (expert) should not be expected to

    have all the knowledge required, and, even if such an expert exists,comparative elicitation from multiple experts is itself a valuable

    knowledge elicitation technique

    Validation process, which is a key to an effective and successful system

    development

    Knowledge Acquisition

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    Knowledge AcquisitionTasks performed by a knowledge

    engineer

    Domain Terminology

    Salient domain features

    ReviseLearn

    Scope of the problem

    Knowledge Sources

    Outline Constrain

    Problem-solving tasks

    Domain objectsSpecify Verify

    Paper Know ledge Base

    Rules and HeuristicsProduce Validate

    Overview Interview

    FocussedInterview s

    Literature Review

    StructuredInterview

    Rule Animation

    Objectives Revision PhaseDiscovery Phase Technique Used

    Consult Textbooks

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    Traditional Approach to Knowledge Acquisition

    In the traditional approach to acquiring knowledge, aknowledge engineer consults reference materials, databases,and human experts.

    The knowledge captured will be both explicit and tacitknowledge.

    Explicit knowledge is acquired through printed material.

    Tacit knowledge originates from human resources. It is thetacit knowledge that never gets quantified into a manual orother accessible form, but resides in the minds of the peoplewho have worked with and developed that information.

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    Tacit Knowledge Acquisition

    Traditional Methods for Tacit knowledge acquisition are Costlybecause at least two (typically) expensive people are involved, i.e.,the domain expert and the knowledge engineer.

    The methods are error prone because people cant easily say what it

    is that they do in a manner that can be understood by others

    Traditional Methods is time-consuming because errors, gaps, andinconsistencies may be difficult to discover, requiring manyinteractions between experts and knowledge engineers to debug afield-ready application.

    Clearly, costs must be reduced, errors eliminated, and developmenttime shortened. An approach to solve these issues is to augment theknowledge engineer with a framework for knowledge acquisition.

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    Explicit Knowledge Acquisition

    There are often several sources of explicit knowledge

    1. Literature : These documents can also be helpful in defining and clarifying theterminology of the problem domain.

    2. Company Policy Manuals and Regulations : These documents are generallyformatted and organized in a manner that is analogous to the format and organizationof business rules specifications.

    3. Reports, Memos and Guidelines : These types of documents are generally notformatted and organized in a manner that is useful to the elicitation process.

    4. Published Books and Journal Articles : Published sources are generally the leastuseful forms of documentation to the elicitation process.

    5. Existing Application Code

    6. Database-Stored Procedures

    7. Program Source Code

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    Tools for KnowledgeAcquisition

    -Rahul sharma

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    Elicitation Methods

    Manual Based on interview Track reasoning process Observation

    Semiautomatic Build base with minimal help from

    knowledge engineerAllows execution of routine tasks with

    minimal expert input

    Automatic

    Minimal input from both expert andknowled e en ineer

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    Manual Methods

    Interviews

    Structured

    Goal-orientedWalk through

    Unstructured

    Complex domains

    Data unrelated and difficult to integrate

    Semistructured

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    Manual Methods

    Process tracking

    Track reasoning processes

    Protocol analysis Document experts decision-making

    Think aloud process

    Observation Motor movements

    Eye movements

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    Manual Methods

    Case analysis

    Critical incident

    User discussions

    Expert commentary

    Graphs and conceptual modelsBrainstorming

    Prototyping

    Multidimensional scaling for distance matrix

    Clustering of elements

    Iterative performance review

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    Semiautomatic Methods

    Repertory grid analysis Personal construct theory

    Organized, perceptual model of experts knowledge Expert identifies domain objects and their attributes Expert determines characteristics and opposites for each

    attribute Expert distinguishes between objects, creating a grid

    Expert transfer system

    Computer program that elicits information fromexperts Rapid prototyping Used to determine sufficiency of available

    knowledge

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    Semiautomatic Methods, continued

    Computer based tools features:

    Ability to add knowledge to base

    Ability to assess, refine knowledgeVisual modeling for construction of domain

    Creation of decision trees and rules

    Ability to analyze information flows Integration tools

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    Automatic Methods

    Data mining by computers

    Inductive learning from existing

    recognized casesNeural computing mimicking humanbrain

    Genetic algorithms using naturalselection

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    Neural network (MLP)

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    Model of an artificial neuron

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    General neuro-fuzzy architecture

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    Multiple Experts

    Scenarios Experts contribute individually

    Primary experts information reviewed by

    secondary experts Small group decision

    Panels for verification and validation

    Approaches Consensus methods Analytic approaches

    Automation of process through software usage

    Decomposition

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    Automated Knowledge Acquisition

    Induction

    Activities

    Training set with known outcomes Creates rules for examples

    Assesses new cases

    Advantages

    Limited application

    Builder can be expert

    Saves time, money

    Automated Knowledge

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    Automated KnowledgeAcquisition

    Difficulties Rules may be difficult to understand

    Experts needed to select attributes

    Algorithm-based search process producesfewer questions

    Rule-based classification problems

    Allows few attributes

    Many examples needed Examples must be cleansed

    Limited to certainties

    Examples may be insufficient

    Automated Knowledge

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    Automated KnowledgeAcquisition

    Interactive induction

    Incrementally induced knowledge

    General models Object Network

    Based on interaction with expert

    interviews

    Computer supported Induction tables

    IF-THEN-ELSE rules

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    Practical Application ofFuzzy Logic

    Rakesh kumar,108

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    Fault Analysis

    The fuzzy detection system is developed and tested withnoisy data and with filtered data. It detects on filtereddata i.e. very accurate with no false alarms andnegligible missed alarms.

    Decision Making

    A new decision making method using fuzzy logic isproposed. The objective is to solve behaviour conflicts in

    behaviour-based architectures. Two main problems havebeen identified: how to decide which behaviour shouldbe activated at each instant; and how to combine the

    results from different behaviours into one action.

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    Image Analysis

    fuzzy technique, was chosen for imageenhancement and applied in a very specific fieldof optical measurements

    SchedulingThe scheduling and mapping of the precedence-constrained task graphs of parallel programs toprocessors is considered one of the most crucial NP-

    complete problems in parallel and distributed computingsystems. task scheduling model based on fuzzy logic isproposed

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    . Problem Solving MethodologyIt is used directly and indirectly in a no. of applications

    Fraud Detection

    The system detects probable fraudulent behaviour:

    by evaluating all the characteristics of a provider's claim data inparallel,

    against the normal behaviour of a small( in demographic terms )community.

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    Soft Computing

    Soft computing plays an increasingly important role inmany application areas, including software engineering.

    Intelligent RoboticsThe use of fuzzy logic has resulted in smooth motion

    control robust performance in face of errors in the priorknowledge and in the sensor data and principledintegration between different layers.

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    Speech Recognition

    To apply fuzzy logic to speech recognition is a new attempt in digitalspeech processing. The approach proposed in the paper simplifies thealgorithm in speech recognition and makes the real-time processingtime shorter.

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    Practical Applications

    Captures intuitive, human expressions

    Precision temperature and humidity control

    Straightforward and therefore inexpensiveControl electronics

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    Practical Applications

    Electric Power

    Industrial applications

    Automated Cow Status Monitoring

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    Practical Applications

    Applied to motor control

    Minimization of cycling times

    Fuzzy logic Design

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    Knowledge Representation

    Pallavi Sagne , 92

    Ritu Kushwaha,112

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    Fuzzy logic is a set of mathematical principles forknowledge representation based on degrees ofmembership.

    Unlike two-valued Boolean logic, fuzzy logic is multi-valued. It deals with degrees of membership and degrees of

    truth. Logical values between 0 (completely false) and 1

    (completely true). Accepting that things can be partly true and partly

    false at the same time.

    Range of logical values in

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    Range of logical values inBoolean and fuzzy logic

    (a) Boolean Logic. (b) Multi-valued Logic.

    0 1 10 0.2 0.4 0.6 0.8 100 1 10

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    The classical example in fuzzy sets is tall men.The elements of the fuzzy set tall men are all men, but theirdegrees of membership depend on their height.

    Degree of Membership

    Fuzzy

    Mark

    John

    Tom

    Bob

    Bill

    1

    1

    1

    0

    0

    1.00

    1.00

    0.98

    0.82

    0.78

    Peter

    Steven

    Mike

    David

    Chris

    Crisp

    1

    0

    0

    0

    0

    0.24

    0.15

    0.06

    0.01

    0.00

    ame Height, cm

    205

    198

    181

    167

    155

    152

    158

    172

    179

    208

    Crisp and fuzzy sets of tall

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    C sp a d u y se s omen

    150 210170 180 190 200160

    Height, cmDegree of

    embership

    Tall Men

    150 210180 190 200

    1.0

    0.0

    0.2

    0.4

    0.6

    0.8

    160

    Degree ofembership

    170

    1.0

    0.0

    0.2

    0.4

    0.6

    0.8

    Height, cm

    Fuzzy Sets

    Crisp Sets

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    The x-axis represents the universe of discourse

    The range of all possible values applicable to a chosenvariable.

    In our case, the variable is the man height.

    According to this representation, the universe of mensheights consists of all tall men.

    The y-axis represents the membership value of the fuzzy

    set. In our case, the fuzzy set of tall men maps height

    values into corresponding membership values.

    A fuzzy set is a set with fuzzy boundaries

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    A fuzzy set is a set with fuzzy boundaries.

    Let X be the universe of discourse and its elements be

    denoted as x.

    In the classical set theory, crisp set A of X is defined asfunction fA(x) called the characteristic function of A

    fA(x): X {0, 1}, where

    Ax

    AxxfA

    if0,

    if1,)(

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    In the fuzzy theory, fuzzy set A of universe X is defined byfunction A(x) called the membership function of set A

    A(x): X [0, 1], where A(x) = 1 if x is totally in A;

    A(x) = 0 if x is not inA;

    0 < A(x) < 1 if x is partly inA.

    membership function A(x) equals the degree to which x is anelement of set A.

    This degree, a value between 0 and 1, represents the degreeof membership, also called membership value, of element x inset A.

    Crisp and fuzzy sets of short, average and

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    tall men

    150 210170 180 190 200160

    Height, cmDegree of

    embership

    Tall Men

    150 210180 190 200

    1.0

    0.0

    0.2

    0.4

    0.6

    0.8

    160

    Degree ofembership

    Short Average ShortTall

    170

    1.0

    0.0

    0.2

    0.4

    0.6

    0.8

    Fuzzy Sets

    Crisp Sets

    Short Average

    Tall

    Tall

    Representation of crisp and fuzzy

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    p p ysubsets

    Fuzzy Subset A

    uzziness

    1

    0Cris Subset A uzziness

    (x)

    Typical functions that can be used to represent a fuzzy set

    Sigmoid, Gaussian and pi.

    These functions increase the time of computation.

    In practice, most applications use linear fit functions.

    Abd i

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    AbductionAbduction is a reasoning process that tries to form

    plausible explanations for abnormal observations Abduction is distinctly different from deduction and induction

    Abduction is inherently uncertain

    Uncertainty is an important issue in abductivereasoning

    Some major formalisms for representing and reasoningabout uncertainty Mycins certainty factors (an early representative)

    Probability theory (esp. Bayesian belief networks)

    Dempster-Shafer theory

    Fuzzy logic Truth maintenance systems

    Nonmonotonic reasoning

    Abd ti

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    Abduction

    Definition (Encyclopedia Britannica):reasoning that derives an explanatoryhypothesis from a given set of facts The inference result is a hypothesisthat, if true, could

    explain the occurrence of the given facts

    Examples Dendral, an expert system to construct 3D structure of

    chemical compounds

    Fact: mass spectrometer data of the compound and its

    chemical formula KB: chemistry, esp. strength of different types of bounds

    Reasoning: form a hypothetical 3D structure that satisfiesthe chemical formula, and that would most likely producethe given mass spectrum

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    Medical diagnosis

    Facts: symptoms, lab test results, and other observedfindings (called manifestations)

    KB: causal associations between diseases andmanifestations

    Reasoning: one or more diseases whose presencewould causally explain the occurrence of the givenmanifestations

    Many other reasoning processes (e.g., word sensedisambiguation in natural language process, imageunderstanding, criminal investigation) can also beenseen as abductive reasoning

    Abduction examples (cont.)

    Comparing abduction, deduction,

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    and induction

    Deduction: major premise: All balls in the box are blackminor premise: These balls are from the box

    conclusion: These balls are black

    Abduction: rule: All balls in the box are black

    observation: These balls are black

    explanation: These balls are from the box

    Induction: case: These balls are from the box

    observation: These balls are black

    hypothesized rule: All ball in the box are black

    A => B

    A---------B

    A => BB

    -------------

    Possibly A

    WheneverA then B-------------PossiblyA => B

    Deductionreasons from causes to effectsAbduction reasons from effects to causesInduction reasons from specific cases to general rules

    Characteristics of abductive

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    reasoning

    Conclusions are hypotheses, not theorems (maybe false even ifrules and facts are true) E.g., misdiagnosis in medicine

    There may be multiple plausible hypotheses Given rules A => B and C => B, and fact B, both A

    and C are plausible hypotheses

    Abduction is inherently uncertain Hypotheses can be ranked by their plausibility (if it

    can be determined)

    Characteristics of abductive

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    Characteristics of abductivereasoning (cont.)

    Reasoning is often a hypothesize-and-test cycle Hypothesize: Postulate possible hypotheses, any of which

    would explain the given facts (or at least most of theimportant facts)

    Test: Test the plausibility of all or some of these hypotheses One way to test a hypothesis H is to ask whether something

    that is currently unknownbut can be predicted from Hisactually true If we also know A => D and C => E, then ask if D and E are true If D is true and E is false, then hypothesis A becomes more plausible

    (support for A is increased; support for C is decreased)

    Characteristics of abductive

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    C a acte st cs o abduct ereasoning (cont.)

    Reasoning is non-monotonic That is, the plausibility of hypotheses can

    increase/decrease as new facts are collected

    In contrast, deductive inference is monotonic: itnever change a sentences truth value, onceknown

    In abductive (and inductive) reasoning, somehypotheses may be discarded, and new onesformed, when new observations are made

    Sources of uncertainty

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    Sources of uncertainty

    Uncertain inputs Missing data Noisy data

    Uncertain knowledge Multiple causes lead to multiple effects Incomplete enumeration of conditions or effects Incomplete knowledge of causality in the domain Probabilistic/stochastic effects

    Uncertain outputs Abduction and induction are inherently uncertain Default reasoning, even in deductive fashion, is uncertain Incomplete deductive inference may be uncertain

    Probabilistic reasoning only gives probabilistic results(summarizes uncertainty from various sources)

    Decision making with

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    guncertainty

    Rational behavior: For each possible action, identify the possible

    outcomes

    Compute the probability of each outcome

    Compute the utility of each outcome

    Compute the probability-weighted (expected)utility over possible outcomes for each action

    Select the action with the highest expected utility(principle ofMaximum Expected Utility)

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    Bayesian reasoning

    Probability theory

    Bayesian inference

    Use probability theory and information about

    independence Reason diagnostically (from evidence (effects) to

    conclusions (causes)) or causally (from causes toeffects)

    Bayesian networks Compact representation of probability distribution

    over a set of propositional random variables

    Take advantage of independence relationships

    Other uncertainty representations

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    Other uncertainty representationsDefault reasoning Nonmonotonic logic: Allow the retraction of default beliefs if

    they prove to be false

    Rule-based methods Certainty factors (Mycin): propagate simple models of belief

    through causal or diagnostic rules

    Evidential reasoning Dempster-Shafer theory: Bel(P) is a measure of the evidence

    for P; Bel(P) is a measure of the evidence against P;together they define a belief interval (lower and upperbounds on confidence)

    Fuzzy reasoning Fuzzy sets: How well does an object satisfy a vague

    property?

    Fuzzy logic: How true is a logical statement?

    Uncertainty tradeoffs

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    Uncertainty tradeoffs

    Bayesian networks: Nice theoretical properties

    combined with efficient reasoning make BNs verypopular; limited expressiveness, knowledgeengineering challenges may limit uses

    Nonmonotonic logic: Represent commonsense

    reasoning, but can be computationally very expensiveCertainty factors: Not semantically well founded

    Dempster-Shafer theory: Has nice formalproperties, but can be computationally expensive,and intervals tend to grow towards [0,1] (not a very

    useful conclusion)Fuzzy reasoning: Semantics are unclear (fuzzy!),but has proved very useful for commercialapplications