““You haven't lost You haven't lost your smile at all, it's your smile at all, it's

right under your right under your nose. You just forgot nose. You just forgot

it was there.”it was there.”

Smile!Smile!

MODULE-2 : UNIT-9

ATIFICIAL INTELLIGENCE

AND INTELLIGENT SYSTEMS IN BUSINESS

TOPICS IN UNIT-9•ARTIFICIAL INTELLIGENCE

•INTELLIGENT SYSTEMS

•EXPERT SYSTEMS

•OTHER INTELLIGENT SYSTEMS

•INTELLIGENT AGENTS

•VIRTUAL REALITY

•ETHICAL & GLOBAL ISSUES OF INTELLIGENT SYSTEMS

1. Intelligence = Information communicated. = Ability to understand and

communicate information.

2. Nature of Human Intelligence :- Found in human beings,- Can be increased by education

and training.- Degree varies in persons

NATURE OF HUMAN INTELLIGENCE

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 knowledge acquired from experience.

(b) Handle complex situations,

Contd.

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).

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

MAJOR A.I. APPLICATION AREAS

A. I.

COGNITIVE (KNOWLEDGE)

SCIENCE APPLICATIONS

ROBOTICS APPLICATIONS

NATURAL INTERFACE

APPLICATIONS

COGNITIVE (KNOWLEDGE ) SCIENCE APPLICATIONS

1. Expert Systems : Computer system that stores knowledge and makes inferences similar to reasoning 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)

ROBOTICS APPLICATIONS

1. Visual perception.

2. Tactility (touch).

3. Dexterity (cleverness).

4. Locomotion.

5. Navigation

NATURAL INTERFACE APPLICATIONS

1.Natural Language Processing: Ability to communicate with computers in human language.

2.Speech Recognition : Recognition and understanding 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.

INTELLIGENT SUPPORT SYSTEMSFUNCTIONS AND TYPES

1. Capturing Tacit Knowledge :

- Expert Systems,

- Case-based reasoning,

- Fuzzy Logic

2. Knowledge Discovery :

- Neural Networks

- Data Mining

3. Generating Solutions to Very Large and Complex Problems :

- Genetic Algorithm

EXPERT SYSTEMS1. Definition : It consists of hardware and

software 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’,

EXPERT SYSTEMS (contd.)APPLICATION AREAS

1. Decision Management (make or buy, credit limit, 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

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

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 a bank balance of Rs 50000

2. Hari has an urban background.

KNOWLEDGE BASE

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 the rule base.

2. Inference Engine : Strategies used to search the rule base :-

(a) Forward chaining,

(b) Backward chaining

contd.

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 Chaining

1. 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 per 1 above.

3. Continue the process until correct conclusion is identified.

CASE-BASED REASONING

1. Purpose : To adapt successful solutions used in the past (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 those features,

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

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.

NEURAL NETWORKS

1. Definition : A complete system that can simulate the functioning of a human brain.

2. Like human brain, a neural net has a large number of sensing and processing nodes that continuously interact with each other.

NEURAL NETWORKS (contd.)FEATURES OF NEURAL NETWORKS

• Ability to retrieve information even if some 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 for which all information is not available.

• Have generalized capability to learn.

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.

DIFFERENCE BETWEEN EXPERT SYSTEMS AND NEURAL NETWORKSEXPERT SYSTEMS NEURAL NETWORKS

1.Seeks to emulate a or model human expert’s 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 of biological brain.

2. Do not aim to solve specific problems.

3. Intelligence is put in to hardware in the form a generalized capability to learn.

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.

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.

INTELLIGENT AGENTS1. Definition : It is a software entity that senses its environment

and then carries out some operations on behalf of a user (or a program), with certain degree of autonomy, and in so doing employs knowledge or representations of user’s 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

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

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.

ETHICAL & GLOBAL ISSUES OF 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 – expert’s knowledge

4.Misuse of robotic capabilities.

ETHICAL & GLOBAL ISSUES OF INTELLIGENT SYSTEMS (contd.)

LEGAL ISSUES

1. 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?

ETHICAL & GLOBAL ISSUES OF 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 expert advice in areas of medicine, safety, agriculture, and crime fighting.

MODULE-2 : UNIT-9

ATIFICIAL INTELLIGENCE

AND INTELLIGENT SYSTEMS IN BUSINESS