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Page 1: Artificial intelligence & expert systems
Page 2: Artificial intelligence & expert systems

ARTIFICIALINTELLIGEN

CE

Page 3: Artificial intelligence & expert systems

Different perspectives Definition

Intelligence artificial intelligence is making machines

"intelligent" -- acting as we would expect people

to act.

Research "artificial intelligence is the study of how to make

computers do things which, at the moment,

people do better"

Business AI is a set of very powerful tools, and

methodologies for using those tools to solve

business problems.

Programming AI includes the study of symbolic programming,

problem solving, and search.

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Types

Expert systems obtained information from experts are

converted in to codes based on thumb rules

to be followed

Neural networks Animals and people naturally distinguish

many kinds of complex patterns, such as the

sound of a bird or the shape of a face. Some

kinds of computer programs "learn" things in

ways that mimic the behaviour of biological

nerve cells.

Motion controllers: Some researchers study how people and

animals move about and manipulate objects

to improve the motion of robots and

machines

Genetic algorithms : Genetic algorithms take a competitive and

repetitive approach to problem-solving.

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Tasks Application

Formal Tasks mathematics, games

Mundane tasks perception, robotics, natural language,

common sense reasoning)

Expert tasks financial analysis, medical diagnostics,

engineering, scientific analysis, and other

areas

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Machine Learning!

Machine learning is a scientific discipline concerned with the design

and development of algorithms that allow machines to mimic human

intelligence.

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How Does Artificial Intelligence

learn?

• It’s a program that learn by making mistakes so that mistakes are not repeated.

Failure driven learning

• Here a teacher has to teach. But communication is the problem. Hence code languages are used

Learning by being told

• Here it does not work toward goal but just keeps on acquiring information to learn so data base keeps on increasing.

Learning by exploration

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Turing Test

It is a test of machines ability to demonstrate

intelligence

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Human Intelligence v/s Artificial

intelligence

Human Intelligence Artificial Intelligence

• Humans are fallible

• They have limited

knowledge bases

• Information processing of

serial nature proceed

very slowly in the brain as

compared to computers

Humans are unable to

retain large amounts of

data in memory.

No “common sense”

Cannot readily deal with

“mixed” knowledge

May have high

development costs

Raise legal and ethical

concerns

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Human Intelligence v/s Artificial

intelligence

Intuition, Common

sense, Judgment,

Creativity, Beliefs etc

The ability to

demonstrate their

intelligence by

communicating

effectively

Plausible Reasoning

and Critical thinking

Ability to simulate

human behavior and

cognitive processes

Capture and preserve

human expertise

Fast Response. The ability to comprehend large amounts of data quickly

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Artificial Intelligence VS Conventional

Computing

Artificial Intelligence

Conventional

Computing

AI software uses the

techniques of search

and pattern matching

Programmers design

AI software to give the

computer only the

problem, not the steps

necessary to solve it

Conventional computer software follow a logical series of steps to reach a conclusion

Computer programmers originally designed software that accomplished tasks by completing algorithms

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Expert Systems

An expert system is software that attempts to

reproduce the performance of one or more

human experts, most commonly in a specific

problem domain. Ex,

Play chess

Help in financial decisions

Configuration of computers etc

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Major Components Knowledge base - a declarative representation of the

expertise, often in IF THEN rules

Working storage - the data which is specific to a problembeing solved

Inference engine - the code at the core of the system Derives recommendations from the knowledge base and

problem-specific data in working storage

User interface - the code that controls the dialog between theuser and the system

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Knowledge

Base

User

Interface

Inference

Engine

Working

Storage

Domain

Expert

Knowledge

Engineer

System

Engineer

USER

Expertise

Encoded

Expert

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Terms Importance

Domain expert The development of accounting software requires

knowledge in two different domains, namely accounting and

software

Knowledge engineer KE is an engineering discipline that involves

integrating knowledge into computer systems in order to

solve complex problems normally requiring a high level

of human expertise.

Systems engineering It is an interdisciplinary field of engineering that focuses on

how to design and manage complex engineering projects

over their life cycles. Systems engineering deals with work-

processes, optimization methods, and risk

management tools in such projects.

User User will be consulting with the system to get advice which

would have been provided by the expert

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Characteristics Of Expert

Systems The Highest level of expertise

Right on time reaction

Accepting the incorrect reasoning

Good reliability

Easily understood

Flexible

Symbolic reasoning

Heuristic reasoning

Making mistakes

Expanding with tolerable difficulties

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Advantages v/s Disadvantages

Advantages Disadvantages

Consistent answers for repetitive decisions, processes and tasks

Holds and maintains significant levels of information

Encourages organizations to clarify the logic of their decision-making

Never "forgets" to ask a question, as a human might

Lacks common sense Cannot make creative

responses as human expert

Domain experts not always able to explain their logic and reasoning

Errors may occur in the knowledge base

Cannot adapt to changing environments

Costly to develop Legal & ethical dilemma Difficult to use

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