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A brief introduction to Artificial Intelligence & Expert Systems
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ARTIFICIALINTELLIGEN
CE
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.
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.
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
Machine Learning!
Machine learning is a scientific discipline concerned with the design
and development of algorithms that allow machines to mimic human
intelligence.
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
Turing Test
It is a test of machines ability to demonstrate
intelligence
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
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
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
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
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
Knowledge
Base
User
Interface
Inference
Engine
Working
Storage
Domain
Expert
Knowledge
Engineer
System
Engineer
USER
Expertise
Encoded
Expert
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
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
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