knowledge management

A.A.S.T.M.T. - IS Masters – MIS, 2010

Prepared & Presented by:

Abdullah RadyLamis Labib

Mohamed IsmailMohamed Zawra

Khalid Zawra

Knowledge Management for the Digital Firm KMS

Management

IS Masters – MIS – Knowledge Management, 2010

Agenda

OverviewConcept of KnowledgeDefining Knowledge ManagementKBDSSKnowledge Creation & ArchitectureKM System Life CycleKnowledge Capturing Knowledge TestingCase StudyDemo

Management


Management


KM IN Boeing

How is Boeing using knowledge management systems to execute its business model and business strategy?

Management


Management


The Need to access & Share Knowledge

Management


Sharing Knowledge


KNOWLEDGE

Management

Presenter

Presentation Notes

Knowledge can be captured and stored in books, reports, databases and images. Knowledge is also what is transferred between people when we talk, collaborate and learn; Knowledge is created as part of SOCIAL interaction.. Knowledge is what we have in our heads, it is elusive, it is our know-how our expertise our capability. Finally knowledge is an asset – capable of earning money or giving us the competitive edge – it is about doing things better.


Management

Skills Talents

HeuristicsExperience

Presenter

Presentation Notes

Skills: Things that can be learnt and there is a tangible measure. Talents: able to be spotted and fostered by leadership or management programs but otherwise is inherent. Experience: It can be both collective experience and individual experience i.e. Some of this may never be able to be replicated or codified. Heuristics: Experts make decisions using heuristics, or rules of thumb. Some of the rules used may be capable of being documented over time.


Basic K. Related Definitions

Experience: knowledge acquired over time of actual practice, leading to superior understanding or mastery.

Heuristics: experience-based techniques for problem solving, learning, and discovery.

Common Sense: what people in common would agree on.



Intelligence: capacity to acquire and apply knowledge.

Ability MemoryLearning

Presenter

Presentation Notes

An intelligent person has the ability to think and reason.


Key Attributes of Intelligence

Ability to understand & use language.

Memory: to store and retrieve relevant experience at will.

Learning: is knowledge or skill acquired by instruction or study.

Presenter

Presentation Notes

An intelligent person has the ability to think and reason.



Learning: knowledge acquired by:-– Instruction,– Study, – Experience,– Discovery.


Types of Learning

Learning by Example: incorporates specially constructed examples rather than a broad range of experience.

Learning by Experience: a function of time and talent.


Types of Learning

Learning by Discovery: undirected approach in which humans explore a problem area with no advance knowledge of what their objective is.


Samuel Johnson

“Knowledge is of two kinds,

We know a subject ourselves,

or we know where we can find information upon it.”


From Data to Knowledge

Data Information Knowledge

Processing+

Experience

[Raw facts] [Understanding Relations] [Understanding Patterns]

+ Interpretation

Presenter

Presentation Notes

Data: symbols� Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions� Knowledge: application of data and information; answers "how" questions� Understanding: appreciation of "why“� Wisdom: evaluated understanding.


From Data to Knowledge

Wisdom

Knowledge

Information

Data[Algorithmic]

[Non-Algorithmic]

[Programmable]

[Non-Programmable]

Presenter

Presentation Notes

Data are unprocessed facts. However, the meaning one brings to the evaluation of data becomes information which, in turn, could add to one’s knowledge. Information is an aggregation of data that makes decision making easier. It is reformatted or processed data. A step higher in abstraction than information is knowledge.


Most significant KM challenges

1.Defining the purpose and focus of a KM strategy for our unique needs

2.Getting leadership to support and commit to knowledge management plan

3.Getting staff to support and use knowledge management approach

4. Developing effective human resource policy to support knowledge workers

Evolution of KM Technologies


Knowledge

is the confident understanding of a subject, potentially with the ability to use it for a specific purpose.

It is “know-how” or a familiarity with how to do something and perform a specialized task.


Shallow and Deep Knowledge

Shallow indicates minimal understanding of problem area.

Deep indicates knowledge built through years of experience.


Common Sense as Knowledge

Its a collection of personal experience and facts acquired over time.

* type of knowledge that humans tend to take for granted


Knowledge as Know-How

Know-how: accumulated lessons of practicalexperience.

Know-how knowledge is represented in terms of heuristics rules based on experience

Know-how distinguishes an expert from a novice


Knowledge

Facts Rules

procedural heuristics

Presenter

Presentation Notes

Knowledge is an accumulation of facts and rules (procedural & heuristics)


Knowledge (Cont.)

Fact: statement of some elements of truth about a subject or domain.

Procedural rule: describes a sequence of relations relative to a domain.

Heuristic rule: based on years of experience.*generally operates in form of IF/THEN statements.

Presenter

Presentation Notes

A fact is a statement that relates a certain element of truth about a subject matter or domain. A rule describes a sequence of relations relative to the domain or subject matter.


Reasoning

Reasoning by analogy Formal

Reasoning

Deductive methods

Inductive methods

Case-based Reasoning


Reasoning

1. Reasoning by analogy: relating one concept to another.

2. Formal reasoning: using deductive or inductive methods.

3. Cased-based reasoning: reasoning from relevant past cases.

Presenter

Presentation Notes

Reasoning by analogy example: “CD is analogous to DVD. Both are used to store data”


Formal Reasoning

a. Deductive methods: generating new knowledge from pre-defined knowledge.

It deals with exact facts and conclusions.

A B C A C

> > >

Presenter

Presentation Notes

Deductive reasoning takes known principles (exact facts) and applies them to instances to infer an exact conclusion. Example: IF “A” is taller than “B” AND “B” is taller than “C”, THEN “A” is taller than “C”. This type of reasoning is used in knowledge bases to capture this type of human expertise.


Formal Reasoning

b. Inductive methods: reasoning from a set of facts or individual cases to general conclusion.

Presenter

Presentation Notes

Inductive reasoning, or induction, is reasoning from a specific case or cases and deriving a general rule. It draws inferences from observations in order to make generalizations. Example: IF I Like book “A”, and I like book “B”, and I like book “C”, THEN I like reading. Inference can be done in four stages: Observation: collect facts, without bias. Analysis: classify the facts, identifying patterns o of regularity. Inference: From the patterns, infer generalizations about the relations between the facts. Confirmation: Testing the inference through further observation. In an argument, you might: Derive a general rule in an accepted area and then apply the rule in the area where you want the person to behave. Give them lots of detail, then explain what it all means. Talk about the benefits of all the parts and only get to the overall benefits later. Take what has happened and give a plausible explanation for why it has happened. Inductive arguments can include: Part-to-whole: where the whole is assumed to be like individual parts (only bigger). Extrapolations: where areas beyond the area of study are assumed to be like the studied area. Predictions: where the future is assumed to be like the past.


Nature of Knowledge

1. Explicit (codified) knowledge digitized in books, documents, reports, memos..

2. Tacit (implicit) knowledge embedded in human mind through experience and jobs.

Presenter

Presentation Notes

Explicit or codified knowledge refers to knowledge that is transmittable in formal, systematic language. Tacit knowledge has a personal quality, which makes it hard to formalize and communicate.


The Nature of Knowledge

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Easier to replicate

Leads to competency

Harder to articulate

Harder to transfer

Harder to steal

Highercompetitive advantage

Contributes to efficiency

Easier to document and share

20%

80%

Explicit

Tacit

[clear]

[implied]


From Tacit to Explicit

My total Knowledge

What I can tell or show

What I can write or record

My Knowledge transferred to readers, watchers or listeners

- KM - KM

- KM


From Explicit to Tacit

What I read or observe

What I can connect to,

What I know

Knowledge from practice,

coaching

Knowledge from reflection & dialogue

with practitioners/ mentor

+ KM + KM

+ KM


EXPLICIT AND TACIT KNOWLEDGE

Oral Communication“Tacit” Knowledge

Information Request

Information Feedback

“Explicit” Knowledge

50 – 95%

5%


Knowledge Transformation Processes

Soci

aliz

atio

nExternalization

Internalization

Combination


Nonaka’s Model of Knowledge Creation and Transformation

TACIT TO TACIT(SOCIALIZATION)

e.g., Individual and/or Team Discussions

TACIT TO EXPLICIT(EXTERNALIZATION)

e.g., Documenting a Team Meeting

EXPLICIT TO TACIT(INTERNALIZATION)

e.g., Learn from a report and Deduce new ideas

EXPLICIT TO EXPLICIT(COMBINATION)

e.g., Create a Website from some form of explicit

knowledge; Email a Report


Tacit to tacit communication (Socialization):Takes place between people in meetings or in team discussions.

Tacit to explicit communication (Externalization):Articulation among people trough dialog (e.g., brainstorming).

Explicit to explicit communication (Communication):This transformation phase can be best supported by technology. Explicit knowledge can be easily captured and then

distributed/transmitted to worldwide audience.Explicit to tacit communication (Internalization):

This implies taking explicit knowledge (e.g., a report) and deducing new ideas or taking constructive action.

One significant goal of knowledge management is to create technology to help the users to derive tacit knowledge from explicit knowledge.

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From Procedural to Episodic Knowledge

1. Procedural Knowledge2. Declarative Knowledge3. Semantic Knowledge4. Episodic Knowledge

Shallow Knowledge

Deep Knowledge


Procedural Knowledge

Is an understanding of how to do a task, or carry out a procedure.

Presenter

Presentation Notes

Typing a shoelace is automatic after a person performs the task a number of times. It is procedural knowledge, in that it involves an understanding of how to do a task or a procedure. It is essentially motor in nature.


Declarative Knowledge

An awareness knowledge in which the expert is conscious. E.g. the electrical system of a car, if the

headlights are dim then the battery is faulty.

Presenter

Presentation Notes

a causal relationship between a loose battery cable and dim headlights is declarative or shallow knowledge.


Semantic Knowledge

A deeper knowledge, highly organized, Include major concepts, facts and relationships.

Back to the electrical system of a car example; Semantic knowledge about the system would consist of understanding about the battery, battery cables, lights, ignition system…etc.

as well as the interrelationships among those things.

Presenter

Presentation Notes

Debugging a computer program is pretty much semantic knowledge, depending on the programming language and the level of complexity of the program. Generally, in programming, debugging knowledge is hierarchically organized knowledge of relationships among facts. It could also be episodic knowledge, in that looking at segments of the program, the programmer uses past experience with similar program segments to determine the likely solution to a bug or logical error. In this case, it is experiential information that is chunked by episodes.


Episodic Knowledge

Knowledge based on experiential information.

The longer a human expert takes to verbalize his knowledge, the more episodic it is.

Presenter

Presentation Notes

Episodic knowledge is knowledge based on experiential information chunked as an entity and retrieved from long-term memory on recall. It is synonymous with deep knowledge. For example, a professor with years of consulting experience tends to teach by scenarios or by examples. Such a person doesn’t have to think long about citing an episode to illustrate a point.


WHAT IS KNOWLEDGE MANAGEMENT?

Process of capturing and making use of an organization’s collective expertise anywhere in the business.

Doing the right thing, NOT doing things right.

Knowledge creation, dissemination, upgrade, and apply toward organizational survival.

Part science, part art (intangible assets use), part luck


Knowledge Management

Systematic approaches to help information and knowledge emerge and flow to the right people at the

right time to create value.

Presenter

Presentation Notes

Before moving on, it is probably a good idea to set forward our definition of “ORGANIZATIONAL KNOWLEDGE.”


Knowledge Management in Action

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UseCreate

Collect

Adapt

Review

IdentifyShare

The chain won’t work if any link is broken.


OVERLAPPING FACTORS OF KM

Knowledge

PEOPLE

TECHNOLOGY

ORGANIZATIONALPROCESSES


OVERLAPPING FACTORS OF KM


Knowledge Management Tree


Case Example ” WebMD“


Integration across ….

Across sub-systems



Across dimensional scales

Organism (7)

OrganSystem(6)

Organ (5)

Tissue (4)

Cell (3)

Molecule (2)

Atom (1)

C CH H

H H

Across Temporal scales



Across DisciplinesMedicine

BioEngineering

Biology


KM SYSTEM LIFE CYCLE

IS Masters – MIS – Knowledge Management, 2010 64

KM SYSTEM LIFE CYCLE

Create

KnowledgeOrganization

Collect

Organize

RefineDisseminate

Culture

Leadership

Techno-logy Intelligence

Maintain

Competition

KnowledgeManagementProcess KM Drivers


KM System Development Life Cycle

• Evaluate existing infrastructure• Form the KM team• Knowledge capture• Design KM blueprint (master plan)• Test the KM system• Implement the KM system• Manage change and reward structure• Post-system evaluation


Comparison of the development life cycle of a conventional ISLC and

KMLCRecognition of need Evaluate existing infrastructure

Systems analysis Form the KM team

Logical design Knowledge Capture

Physical design (coding) Design KM Blueprint

Testing (corrections to previous step) Verify and validate KM system(corrections to previous step)

Implementation (install, user training) Implement the KM system

Conversion, Operation & Maintenance Manage change & reward structure

Post system evaluation ISLC KMLC


Evaluate Existing Infrastructure

System justification Are experts available and willing to help in building a KM system?. Does the problem in question require years of experience and cognitive reasoning to solve?

When undergoing knowledge capture, can the expert articulate how problem will be solved?. Are the tasks non algorithmic?

Is there a champion in the house?

How critical is the knowledge to be captured?Scoping and evaluating Boundaries of the KS Limits breadth and depth of the project within financial, human resource, sales n marketing and

operational constraints.System feasibilityDoableaffordableappropriatepracticable

Presenter

Presentation Notes

Is the project doable? Can it be completed within reasonable time? Is it affordable? Do the system potential benefits justify the cost of development? Is it appropriate? Just what can the firm expect to get out of it? Is it practicable? How frequently would the system be consulted?


KM Team Formation

Experts

KNOWERS

CHAMPION

KNOWLEDGE DEVELOPER

KNOWLEDGE BASE

InteractiveInterface

Solutions

UserAcceptance

RulesTesting

Knowledge

SupportFeedback

Prototypes

ProgressReports

Demos

Presenter

Presentation Notes

KM team should be formed and this team stays until the final implementation of the KM system. Identify the key units, departments, branches, etc within the scope of the KS. One or more expert should represent each unit in the prospective KM system for the duration of the project.


Knowledge Capture and Transfer Through Teams

Team performsa specialized task

Knowledge transfer method selected

Evaluate relationship between action and outcome

Outcome Achieved

Knowledge Developer

Knowledge stored in a form usable by others in the organization

Feedback


Design of the KM BlueprintKey layers of a KM system

User Interface Via BrowserPart of the Internet

Authentication/ security layer(includes access identification, Firewalls and user recognition)Internal layer that the company IT controls

Collaborative Agents and filtering(intelligent S/W disseminate news and make intelligent searches)Agent technology is intelligence within a KM system.

Application Layer(collaborative work tools, video conferencing, group decision support tools etc)Upper part of the Data communication network layer.

Transport/Internet Layer(TCP/IP etc)Manage transmission of data between computers.

Physical Layer(Cables, physical wires, modems .. for transmission)Transmission raw data in bit format to destination.

RepositoriesH/D and storage devicesDocuments and files, Knowledge Base, DB, Legacy Applications


Technical Layers of the KM System

User Interface(Web browser software installed on each user’s PC)

Authorized access control(e.g., security, passwords, firewalls, authentication)

Collaborative intelligence and filtering(intelligent agents, network mining, customization, personalization)

Knowledge-enabling applications(customized applications, skills directories, videoconferencing, decision support systems,

group decision support systems tools)

Transport(e-mail, Internet/Web site, TCP/IP protocol to manage traffic flow)

Middleware(specialized software for network management, security, etc.)

The Physical Layer(repositories, cables)

. . . . .

K basesData warehousing(data cleansing,

data mining)

Groupware(document exchange,

collaboration)

Legacy applications

1

2

3

4

5

6

7


Testing the KM System

• Verification procedure:Ensures that the system is right that the programs do what they are

designed to do..Technical performance from the functional perspective

• Validation procedure:Ensures that the system is the right system

checks reliability of the KM system.


Implementing the KM System

• Converting a new KM system into actual operation

• Updating the existing H/W & network

• Training• Quality assurance includes checking for:

– Reasoning errors– Ambiguity– Incompleteness– False representation (false positive and false negative)

Presenter

Presentation Notes

Incompleteness, A statement that represents sum but not all pieces of knowledge is incomplete. For example, Does “adjust the air conditioner” mean to make it cooler or warmer? False representation (false positive and false negative) A statement that incorrectly represents pieces of knowledge. Ambiguity, A statement is ambiguous when it has more than one meaning such as “Sell the bad stock.” which stock is “bad”? What is meant by “bad”? How bad is “bad”? “the small key will open the door.” False positive error, a rule is accepted when it should be rejected. “the small key will not open the door when it should” False negative error, a rule is rejected when it should be accepted. “the small key will open the door when it should not”


Manage change and reward structure

Resisters of Change

• Regular employees (users)

• Troublemakers

• Narrow-minded superstars. IT staff resist any change that they did not initiate or approve in advance.

Resistance via projection, avoidance, or aggression


Post system Evaluation of Change”• How has the KM system changed the accuracy and timely of

decision making?

• Has the new KM system caused organizational changes – e.g. BPR? How constructive the changes been?

• How has the new KM system affected the attitude of the end users?

• How has the new KM system changed the cost of operating the business – low cost leadership strategy? How significant was it?

• Do the solution and advice derived from the new KM system justify the cost of investment?

A.A.S.T.M.T. - IS Masters – MIS, 2010

CAPTURING TACIT KNOWLEDGE

4-77

What Is Knowledge Capture ?

• A process by which the expert’s thoughts and experiences are captured

• A knowledge developer collaborates with an expert to convert expertise into a coded program

• In simple terms, we want to “know” how experts know what they know

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Three important steps

• Use an proper tool or technique to extract information from the expert

• understand the informationand understand the expert’s knowledge and reasoning process

• Use the interpretation to build rules that represent expert’s solutions

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Using a Single Expert

Advantages:• Ideal when building a

simple KM system• A problem in a restricted

domain• Easier to coordinate

meetings• Conflicts are easier to

resolve• Shares more confidentiality

than does multiple experts

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Using a Single Expert (cont’d)

Disadvantages:• Sometimes expert’s knowledge is not

easy to capture• Single expert provides only a single line

of reasoning• Expert knowledge is sometimes

dispersed• Single expert more likely to change

scheduled meetings than experts in a team

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Using Multiple Experts

Advantages:• Complex problem domains benefit

from expertise of more than one expert

• Working with multiple experts stimulates interaction

• Allow alternative ways of representing knowledge

• Formal meetings often a better environment for generating thoughtful contributions

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Using Multiple Experts (cont’d)

Disadvantages:• Scheduling difficulties• Disagreements often occur among

experts• Confidentiality issues• Requires more than one knowledge

developer• Overlapping mental processes can

lead to “process loss”

4-83

Approaching Multiple Experts

•Individual– An extension of single

expert approach

•Primary and secondary– Start with the senior expert

first, on down to others in the hierarchy

•Small groupsEach expert tested against

expertise of others in the group

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Developing a Relationship With Experts

• Understanding the expert’s style

• Prepare well for the session

• Decide where to hold the session

IS Masters – MIS – Knowledge Management, 2010 4-85

Styles of expert’s expressions

Procedure type– methodical approach to the solution

Storyteller– focuses on the content of the domain at the

expense of the solution Godfather

– compulsion to take over the session Salesperson

– spends most of the time explaining his or her solution is the best


Preparing for the session

Should become familiar with the project terminology

review existing materials

Learn the expert’s language


Deciding where to hold the sessions

Beneficial in recording the expert’s knowledge in the environment where he or she works

An important guideline is to make sure the meeting place is quiet and free from interruptions

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The Interview As a Tool• Commonly used in the early

stages of tacit knowledge capture

• The voluntary nature of the interview is important

• Interviewing as a tool requires training and preparation

• Convenient tool for evaluating the validity of information acquired


Types of Interviews

Structured: Questions and responses are definitive. Used when specific information is sought

Semi-structured: Predefined questions are asked but allow expert some freedom in expressing the answers

Unstructured: Neither the questions nor their responses specified in advance. Used when exploring an issue


Variations of Structured Questions

Multiple-choice questions offer specific choices, faster tabulation, and less bias by the way answers are ordered

Dichotomous (yes/no) questions are a special type of multiple-choice question

Ranking scale questions ask expert to arrange items in a list in order of their important or preference

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Guide to a Successful Interview

• Set the stage and establish relationship

• Properly phrase the questions• Question construction is

important• Listen closely and avoid

arguments• Evaluate session outcomes


Ending the Interview Ending the interview requires sensitivity

to the expert’s preferences, use of verbal and non verbal cues.

Nonverbal cues for ending an interview;-Look at watch and uncross legs.-Put cap on pen, close folder gently and

uncross legs.-If taping session, stop taping and rewind

tape.-Stop taking notes and place writing

materials in briefcase.


Ending the Interview

Verbal cues for ending an interview;-This is summary of the session. Do you

have any suggestions?-I think I asked all the questions I had in

mind. I appreciate your time.-My allowed time is up. I know you have

another meeting soon.-This looks to be an informative meeting.

How about scheduling another one.-This covers pretty much what I had in

mind. Did I miss anything! .

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Things to Avoid• Taping a session without advance permission

from the expert• Converting the interview into an interrogation• Interrupting the expert• Asking questions that put the domain expert

on the defensive• Losing control of the session• Pretending to understand an explanation when

you actually don’t• Promising something that cannot be delivered• Bring items not on the agenda

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Errors Made by the Knowledge Developer

• Age effect• Race effect• Gender effect

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Problems Encountered During the Interview• Response bias

Questions like; Isn’t it true.. , Don’t you think.. May get a biased answer “Yes”.

• Inconsistencyoccur when the knowledge developer interviews two domain experts and is inconsistent when asking the questions.The questions and their order should be standardized.

The questions must mean the same thing to all the experts being interviewed.

• Communication difficulties• Hostile attitude

bad chemistry between expert and knowledge developer, an expert is forced in participation, or time wasted on repeated dead ends, etc

• Standardized questions• Lengthy questions

• Long interviewDuration of the interview should last no more than one hour.Expert attention begins to breakdown and quality of thoughts

decrease within long interviews.


Validate Information

Various validation and cross-checks should be applied before captured knowledge can be represented.

For example, one way to cross validate an expert opinions is to ask another expert and check for similarities between the two opinions.

Another way to validate an opinion is to ask the question again at the next session in a different way to see if the expert gives the same answer.


On-Site Observation Process of observing, interpreting, and recording problem-solving behavior while it

takes place by experts. In addition, the knowledge developer asks the expert questions about the problem

solving process. The protocol of observation is more listening than talking. Dose not argue with the expert while performing a task. Avoid giving advices to expert while observing. . The problem here is that some experts don’t like to be observed. Experts fear of ‘giving away’ their experience in a quick look. Observation process can be distracting to others in the setting.


Brainstorming

Unstructured approach to generating ideas about a problem;

invites two or more experts into a session in which discussion are carried out.

The primary goal of brain storming is to think up creative solutions to problems.

All possible solutions are considered equally.

Anything related to the topic can be brought up, and everything is valued.


Brainstorming

Questions can be raised for clarification, but no evaluation is made at the moment.

Idea generation, followed by idea evaluation.

In the evaluation phase, the knowledge developer explains each idea and treats any comments or criticism accordingly.


Brainstorming Procedure Introduce brainstorming session;

explain what is to be accomplished, the role of each participant and the expected outcomes.

Give experts a problem to consider; The problem must be in the experts’ domain of expertise.The knowledge developer must give experts time to think within a reasonable time limits.

Prompt the experts to generate ideas; The experts can do this either by calling out their ideas or by order in which each expert is given a turn to speak..

The knowledge developer must keep pace with the expert.

Watch for signs of convergence; Ideas often trigger counter opinions that should eventually reach a final

solution. If the experts can not agree on the final solution, the knowledge developer must

Call for a vote or a consensus to reach agreement


Electronic Brainstorming Computer-aided approach to dealing

with multiple experts. U-shaped desks hold PCs networked

through a S/W tool that promotes instant exchange of ideas between experts.

Projector, whiteboards, and printers are also a part of the infrastructure environment for electronic brainstorming.

Begins with a pre-session plan that identifies objectives and structures agenda, which is presented to experts for approval.


Electronic Brainstorming Allows two or more experts to

provide opinions through PCs without having to wait their turn.

The S/W displays the comments or suggestions on a huge screen without identifying the source.

Protects shy experts and prevents tagging comments to individuals.

The overall benefits include improved communication, effective discussion of sensitive issues, and consider recommendations for action.


Protocol Analysis Think-aloud method.

How each expert arrived at the solution through verbalization.Expert keeps talking, speaking out loud while solving a problem

Effective source of information on cognitive processesMakes expert cognizant of the processes being described; it is a cognitive approach to problem solving.

Provides rich information that is very useful to knowledge capture and representation.

“there are other ways to reach the same solution”


Delphi Method

“When two or more heads are more numerous than one”

Another tool used for tacit knowledge capture.

A survey of experts. Experts are polled concerning a given problem.

A series of questionnaires used to pool experts’ responses in order to solve a difficult problem.

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Delphi Method Responses are usually anonymous and are

collected asynchronously, either my mail, e-mail, or online survey.

During each round the results of the previous questions is fedback to participants who are then asked to revise and consolidate their answers even more.

After several rounds the solicited experts may arrive as a consensus or the researchers may average final responses toward a conclusion.

This method is a powerful and efficient way of drawing on distributed expertise at low cost, time, and inconvenience


What is Knowledge Base-Systems ?

system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action.

Is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge.


Advantages & Disadvantages of KB

Advantage• make up for shortage of experts, spread expert’

knowledge on available price.• increase expert’ ability and efficiency.• preserve know-how.• can be developed systems unrealizable with traditional

technology .• are available permanently.• able to work even with partial, non-complete data.• able to give explanation.


Cont.(Advantage & Disadvantage)

Disadvantage

• Their knowledge is from a narrow field, don’t know the limits.

• The answers are not always correct (advices have to be analyzed!).

• Don’t have common sense (greatest restriction) → all of the self-evident checking have to be defined.


Knowledge-Based DSS

Advanced DSS are equipped with a component called a knowledge-based management subsystem that can supply the required expertise for solving some aspects of the problem


Traditional DSS Components

User

User Interface

DBMS MBMS

KBS3KBS2

KBS1

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INTEGRATING DSS AND KNOWLEDGE MANGEMENT


Framework for INTEGRATING DSS AND KNOWLEDGE MANGEMENT


Traditional computing environment vs.

Intelligent Agents computing environment

Traditional computingIssue Commend

Display Result

Intelligent Agents computingIssue Command & delegate task(Monitor xx stock price)

Agent Monitor

Share Result Computing Stock (xx price dropped 1 point)

Request advises(purchase stock??)

Presenter

Presentation Notes

Important


Functions of KB-DSS

KB-DSS can provide the following:– An interface to support man-machine cooperation

during problem solving– Support access to relevant information during

problem solving– Support problem recognition– Support problem structuring


AUTOMOBILE DIAGNOSTIC SYSTEM


computer-based Expert Systems

• This presentation shows you how a computer-based expert system emulates the behavior of a human advisor, introduces the activities that must be accomplished to build expert systems.


Automobile diagnostic system

• To introduce terms like expert and expertise as they are relevant to expert systems, let's suppose you have been unable to start your car to go to work and you call your favorite mechanic. The dialog might continue something like this...



Hey Sambo, this is AbassMahmoud. My car wouldn't start this

morning and I need some help...

Good morning this is SamboAuto Repair



It turns over OK, but it just won't start.

What happens when you turn the key in the

ignition to try to start the car?

Here is the the beginning of the diagnostic telephone "interview" with your mechanic...



Well, now that you mention it - I'm not

certain the tank is empty, but it probably is.

Hmmm...are your sure that you aren't out of

gas?

Based on your input that the starter operates, your mechanic can abandon a number of hypotheses related to electrical problems. Now the expert is evaluating another possible explanation...



No, I turned it over for a long time, but didn't smell

anything.

As you crank the starter, do you smell gas?

At this point, your mechanic is attempting to confirm the new hypothesis...



Thanks for the advice. Mind telling me how you reached your conclusion?

Based on what you've told me, I'm almost

certain your car is out of gas.

Your mechanic now has enough evidence to diagnose the problem. Once you've heard the recommendation, you might want an explanation of how the conclusion was obtained...



When a car won't start my initial suspicion is that the battery is dead, the starter has failed or some other electrical problem

exists. Your input that the starter operates makes it more likely that no fuel is getting to the

engine. Although you are not sure that the gas tank is empty, the fact that you don't smell gas

when the engine turns over supports my conclusion that you

are out of gas.

You solved your automotive problem by consulting with an expert. Let's take a look at the definition of expertise relevant to expert systems and the attributes of an effective consultant a computer will have to emulate to substitute for a human advisor...


What's an expert?

• An expert is one who possesses specialized skill, experience, and knowledgethat most people do not have along with the ability to apply this knowledge using tricks, shortcuts, and rules-of-thumbto resolve a problem. An expert's advice has to be good enough most of the time for the expert to keep his or her reputation, but is not expected to be perfect or even the globally best available to be considered useful


What are the attributes of effective consultants and

consulting?Consulting is goal oriented

A good consultant is efficient

Consultants are able to work with imperfect information

Good consultants justify their recommendations by explaining their reasoning


the attributes of effective consultants and consulting

• Here's an illustration of each of these attributes from the auto diagnosis example...


Consulting is goal oriented

It turns over OK, but it just won't start.

What happens when you turn the key in the ignition

to try to start the car?

The objective in calling your mechanic is to get a very specific answer to a very specific question. You aren't interested in learning how a fuel injection system works or how to rebuild a starter -- even though your expert would be quite capable of providing this information. The objective of the consultation represents a goal in expert system terminology, and there can be one or many goals to be satisfied during a consultation with a human expert or a computer-based expert system


A good consultant is efficient

Well, now that you mention it - I'm not

certain the tank is empty, but it probably is.

Hmmm...are your sure that you aren't out of gas?

Your answer to the mechanic's first question eliminated a large number of possible problems from further consideration. A good consultant will stop asking questions relevant to hypotheses that can be rejected based on evidence at hand. Because you said the starter operates (eliminating battery problems) it makes no sense to ask you if the headlights light or the horn blows.


A consultation is adaptive

No, I turned it over for a long time, but didn't smell

anything. .

As you crank the starter, do you smell gas?

When the information needed to make a recommendation isn't available, the expert will try other lines of questioning that will help confirm the hypothesis. You weren't sure the gas tank is empty, so the question about smelling gasoline was posed.


Consultants are able to work with imperfect information

Thanks for the advice. Mind telling me how you reached your conclusion?

Based on what you've told me, I'm almost certain your car is out of gas.

You aren't sure the fuel tank is empty, but think it probably is. By combining your less than certain information with the evidence provided by the fact that you don't smell gasoline while the engine turns over, the expert can conclude that you are out of gas with a high degree of certainty.


Consultants justify their recommendations by explaining

their reasoning

When a car won't start my initial suspicion is that the battery is dead, the starter has failed or some other electrical problem

exists. Your input that the starter operates makes it more likely that

no fuel is getting to the engine. Although you are not sure that the gas tank is empty, the fact that you

don't smell gas when the engine turns over supports my conclusion

that you are out of gas.

The application of expertise is not a guessing game. A real expert should be able to explain how evidence was used to evaluate rules-of-thumb to develop recommendations. Given the nature of the consulting process just described, does it make sense to try to deliver advice without the physical presence of an expert?

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