09-Intelligent Support Systems

8/3/2019 09-Intelligent Support Systems

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You haven't lostYou haven't lost

your smile at all, it'syour smile at all, it'sright under yourright under your

nose. You just forgotnose. You just forgotit was there.it was there.

Smile!Smile!


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MODULE-2 : UNIT-9

ATIFICIAL

INTELLIGENCEAND INTELLIGENT

SYSTEMS INBUSINESS


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TOPICS IN UNIT-9

ARTIFICIAL INTELLIGENCE

INTELLIGENT SYSTEMS

EXPERT SYSTEMS

OTHER INTELLIGENT SYSTEMS

INTELLIGENT AGENTS

VIRTUAL REALITY

ETHICAL & GLOBAL ISSUES OF

INTELLIGENT SYSTEMS


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1. Intelligence = Information communicated.

= Ability to understand and

communicate information.

2. Nature of Human Intelligence :

- Found in human beings,

- Can be increased by educationand training.

- Degree varies in persons

NATURE OF HUMAN

INTELLIGENCE


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ARTIFICIAL INTELLIGENCE (A.I.)

1.Definition : It is a field of science and technology

based on disciplines like computer science,biology, psychology, linguistics, mathematics and

engineering to provide intelligence artificially.

2.Purpose of A.I. : Developing machines with

intelligent behaviour.

3. Intelligent Behaviour:

(a) Learn from experience and apply knowledgeacquired from experience.

(b) Handle complex situations,

Contd.


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ARTIFICIAL INTELLIGENCE (A.I.)

3. Intelligent Behaviour (contd.):

(c) Solve problems when important information is missing,

(d) Determine what is important,

(e) React quickly and correctly to a new situation,

(f) Understand visual images using perceptive

systems (see, hear and feel),

(g) Process and manipulate symbols (symbols and 3-D

objects)(h) Be creative and imaginative,

(i) Use heuristics (thumb rules from experience).


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DIFFERENCE BETWEEN NATURAL INTELLIGENCE

(N. I.) AND ARTIFICIAL INTELLIGENCE (A.I.)

S.

N.

ATTRIBUTES

Ability to :

N.I.(HUMAN)

A.I.(MACHINE)

1.

2.

3.

4.

5.

6.

7.

8.

9.

10

Use sensors (eyes, ears, touch, smell)

Be creative and imaginative

Learn from experience

Be adaptive

Afford the cost of acquiring intelligence

Use a variety of information sources

Acquire large amount of external information

Make complex calculations

Transfer information

Make series of calculations rapidly and accurately

HIGH

HIGH

HIGH

HIGH

HIGH

HIGH

HIGH

LOW

LOW

LOW

LOW

LOW

LOW

LOW

LOW

HIGH

HIGH

HIGH

HIGH

HIGH


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MAJOR A.I. APPLICATION AREAS

A. I.

COGNITIVE

(KNOWLEDGE)SCIENCE

APPLICATIONS

ROBOTICSAPPLICATIONS

NATURAL

INTERFACEAPPLICATIONS


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COGNITIVE (KNOWLEDGE ) SCIENCE

APPLICATIONS1. Expert Systems : Computer system that stores

knowledge and makes inferences similar toreasoning by human expert.

2. Fuzzy Logic : Reasoning that deals with

uncertainties or partial information.

3. Genetic Algorithm : Used for finding optimal

solution from large number of possible solutions.

4. Neural Networks : Computer system that can

simulate the functioning of human brain.

5. Intelligent Agents :

- Percepts (Sensors)

- Acts (Actuators)


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ROBOTICS APPLICATIONS

1. Visual perception.

2. Tactility (touch).

3. Dexterity (cleverness).

4. Locomotion.

5. Navigation


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NATURAL INTERFACE

APPLICATIONS1.Natural Language Processing:Ability to

communicate with computers in human language.

2.Speech Recognition : Recognition andunderstanding by a computer of a spoken

language.

3.Multi-sensory Interface :

4.Virtual Reality System : Enables one or more

users to move and react in a computer-simulated

environment.


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INTELLIGENT SUPPORT SYSTEMS

FUNCTIONS AND TYPES

1. Capturing Tacit Knowledge :

- Expert Systems,

- Case-based reasoning,

- Fuzzy Logic2. Knowledge Discovery :

- Neural Networks

- Data Mining3. Generating Solutions to Very Large and Complex

Problems :

- Genetic Algorithm


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EXPERT SYSTEMS

1. Definition : It consists of hardware andsoftware that stores knowledge about a

specific area and makes inferences to act

as an expert consultant in providing

decision support to end user.2. Purpose : Overcomes limitations / non-

availability of human expert by :-

- Coping with new challenges

- Handling many decision variables,

- No Information Fatigue Syndrome,


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EXPERT SYSTEMS (contd.)APPLICATION AREAS

1. Decision Management (make or buy, creditlimit, incentive scheme, customer query,

production query, investment counseling)

2. Diagnostic / troubleshooting,

3. Maintenance / scheduling

4. Design / configuration,

5. Selection / classification,

6. Process monitoring,7. Product development / evaluation,

8. Performance evaluation,

9. New product launch


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EXPERT SYSTEMS (contd.)

1. Success Factors in BES Implementation- Cost effectiveness,

- Selective in scope

- User friendliness

2. Limitations of BES :

- Poor availability of domain human expert,

- Lack of flexibility required by dynamic

environment,- Suited only for limited applications,

- Cannot replace human experts


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COMPONENTS OF EXPERT SYSTEMSA.I. SHELL

E.S. SOFTWARE

INFERENCE

ENGINE

PROGRAMS

USER

INTERFACE

PROGRAMS

USER

INTER

-FACE

KNOWLEDGE

BASE

ACQUISITION

FACILITY

EXPERT

USER

EXPLANATION

FACILITY

RULE BASESYMBOL

(FACTS)

DATABASE

R1 : IF () THEN()

R2 : IF() THEN ()

1. Hari has abank balance ofRs 50000

2. Hari has an

urban

background.

KNOWLEDGE BASE


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How expert systems work?1. Functions of A.I. Shell :

- Be user friendly,

- Quickly generate user interface screens

- Capture the knowledge base,

- Manage the strategies for searching therule base.

2. Inference Engine : Strategies used to search

the rule base :-

(a) Forward chaining,

(b) Backward chaining

contd.


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How expert systems work? (contd.)Forward Chaining

The inference begins with the information (facts)

entered by the user and searches the rule base

to arrive at a conclusion.

Backward Chaining1. Process of starting with conclusion and working

backwards to supporting facts.

2. If facts do not support the conclusion, another

conclusion is selected and tested as per1above.

3. Continue the process until correct conclusion is

identified.


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CASE-BASED REASONING

1. Purpose : To adapt successful solutions used in thepast (cases) to solve new problems.

2. Method : Find the solutions that solved problems

similar to current problem. Then adapt the previous

solution to fit the current problem.

3. Steps in Finding Relevant Cases :

(a) Characterizing the input problem,

(b) Retrieving from memory the cases with thosefeatures,

(c) Picking the case or cases that best match the

input.


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FUZZY LOGIC1. Fuzzy Logic : Reasoning that deals with uncertainties

or partial information.2. Method :

(a) Deals with uncertainties by simulating the process

of qualitative human reasoning.

(b) Allows computers to behave less precisely /

logically.

3. Rationale : Decision making is not always precise,

there are grey areas where terms approximately,

possible and similar are used.

4. Example : In developing marketing strategy, it helps

managers handle uncertainties and fuzziness of data

and information.


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NEURAL NETWORKS

1. Definition : A complete systemthat can simulate the functioning

of a human brain.

2. Like human brain, a neural net

has a large number of sensing

and processing nodes thatcontinuously interact with each

other.


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NEURAL NETWORKS (contd.)FEATURES OF NEURAL NETWORKS

Ability to retrieve information even ifsome neural nodes fail.

Fast modification of data as a result of

new information, Ability to discover relationships and

trends in large databases.

Ability to solve complex problems forwhich all information is not available.

Have generalized capability to learn.


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NEURAL NETWORKS (contd.)APPLICATION AREAS OF NEURAL NETWORKS1. Medicine : For screening of patients (e.g. Papnet)

2. Financial Industry :

(a) Periodic stock performance, bond ratings or corporate

bankruptcies,

(b) Detecting credit card frauds,

3. Other Business Applications of Neural Nets:(a) Pattern classifications (e.g. patterns in sales data)

(b) Predictions, (c) Control, (d) Optimization,

(e) Decision on credit / mortgage applications

(f) Pick duplicate names in mailing lists

4. Scientific Applications :

(a) Hand written character recognition,

(b) Image compression,

(c) Electronic nose.


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DIFFERENCE BETWEEN EXPERT

SYSTEMS AND NEURAL NETWORKS

EXPERT SYSTEMS NEURAL NETWORKS

1.Seeks to emulate a

or model human

experts way of

solving problem.

2.Expert system is

highly specific to a

given problem.3.Expert Systems

cannot be easily

retrained.

1.Do not model human

reasoning. Designed to

imitate the physical

thought process ofbiological brain.

2. Do not aim to solve

specific problems.3. Intelligence is put in to

hardware in the form a

generalized capability to

learn.


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DATA MINING1. Definition : A means of extracting

previously unknown, predictive information

from the base of accessible data in data

warehouse.

2. Purpose : Sophisticated / automated

algorithms are used to :

(a) To discover hidden patterns, correlations,

and relationships among organizational

data,(b) To predict future trends and behaviors,

allowing businesses to make proactive,

knowledge-driven decisions.


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DATA MINING (contd.)3. Functions: Five main functions :

(a) Classification,

(b) lustering,

(c) Association

(d)Sequencing,

(e) Forecasting

4. Applications : Customer retention;campaign management; market, channel &

pricing analysis; customer segmentation

etc.


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INTELLIGENT AGENTS

1. Definition : It is a software entity that senses its environment

and then carries out some operations on behalf of a user (or aprogram), with certain degree of autonomy, and in so doing

employs knowledge or representations of users goals or

desires.

2. Characteristics :

(a) Autonomy capability to work on their own,

(b) Exhibition of goal oriented behavior,

(c) Mobility transportable over networks,

(d) Dedication to a single repetitive task,

(e) Ability to interact with humans, systems and other agents,

(f) Inclusion of a knowledge base,

(g) Ability to learn


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INTELLIGENT AGENTS (contd.)3.Applications of Intelligent Agents :

(a) Information access using search engines,

(b) Decision support and empowerment,

(c) Repetitive office activities,

(d) Mundane personnel activities,

(e) Database search and retrieval agents

(f) Electronic commerce agents

(g) Domain experts,

(h) Management activities


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VIRTUAL REALITY

1. Definition : It is interactive, uses computer generated, three-dimensional graphics, and is delivered to the user through a

head mounted display.

2. Benefits : Many can share and interact in same environment. It

is powerful medium for communications, collaborative

entertainment, and learning.3. Business Applications :

(a) Manufacturing : Training, designing, testing, simulation of

assembly and production,

(b) Transportation : Virtual aircraft mockup, new car design &virtual accidents, air travel simulation,

(c) Finance : View stock prices

(d) Marketing : Store and product display; electronic shopping.


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INTELLIGENT SYSTEMS

ETHICAL AND SOCIAL ISSUES :1.Possibility of power misuse and harm to

people from the use of intelligent

systems,

2.Privacy in knowledge bases,

3.Intellectual property experts

knowledge

4.Misuse of robotic capabilities.


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INTELLIGENT SYSTEMS (contd.)

LEGAL ISSUES1. Responsibility in case of incorrect judgement leading

to damage or disaster,

2. Liability for wrong advice provided by ES?

3. Who owns the knowledge in knowledge base?

4. Who is an expert?

5. Can management force the experts to contribute their

expertise?

6. Should royalties be paid to expert and how much?

7. Value of expert opinion in court when the expertise is

encoded in a computer?


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INTELLIGENT SYSTEMS (contd.)

GLOBAL ISSUES : Global applications :-

1. Foreign Trade : Online ES to advise how to exploit opportunities,

2. Foreign Exchange Transactions : Intelligent systems for FE

transactions,

3. Employee Training : Online training to reduce time,

4. Weather Forecasting : Climatic ES to provide long-range climate

forecasts for global commodity traders.

5. Automatic Language Translation : Very important in global e-

commerce.

6. Others : ES to fight money laundering, ES to provide expertadvice in areas of medicine, safety, agriculture, and crime

fighting.


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MODULE-2 : UNIT-9

ATIFICIAL

INTELLIGENCEAND INTELLIGENT

SYSTEMS INBUSINESS

Documents

09-Intelligent Support Systems