Priti Srinivas Sajja is an Associate Professor in the Department of Computer Science at Sardar Patel University. The document discusses various topics in artificial intelligence including natural vs artificial intelligence, types of AI tests, applications of AI, knowledge representation in AI systems, bio-inspired computing approaches like artificial neural networks, genetic algorithms, and swarm intelligence. It provides examples of different AI techniques and references for further reading.

1. Artificial Intelligence
Priti Srinivas Sajja
Associate Professor
Department of Computer Science
Sardar Patel University
Visit priti sajja.info for detail
Created By Priti Srinivas Sajja 1

2. Artificial Intelligence
Introduction
Introduction Natural intelligence
AI Tests  Responds to situations flexibly.
Applications  Makes sense of ambiguous or erroneous messages.
 Assigns relative importance to elements of a
Data Pyramid situation.
Knowledge  Finds similarities even though the situations might
Based Systems
be different.
Pros and Cons
 Draws distinctions between situations even though
Bio-inspired
there may be many similarities between them.
Example
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3. Artificial Intelligence
Introduction
Introduction Artificial intelligence
AI Tests
Applications
Data Pyramid
Knowledge
Based Systems
Pros and Cons
Bio-inspired
Example
Acknowledgement
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4. Artificial Intelligence
Introduction
Introduction Artificial intelligence
AI Tests
human thought process
Applications heuristic methods
where people are better
Data Pyramid
non-algorithmic
Knowledge
Based Systems
characteristics we associate knowledge using symbols
Pros and Cons
with intelligence
Bio-inspired
Constituents of artificial intelligence
Example
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5. Artificial Intelligence
Introduction
Introduction Artificial intelligence
AI Tests
Applications
Acceptable Extreme
Data Pyramid solution in solution, either
acceptable best or worst
Knowledge time taking 
(infinite) time
Based Systems
Pros and Cons time
Nature of AI solutions
Bio-inspired
Example
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6. Artificial Intelligence
Introduction Testing Intelligence Turing test will fail to test
for intelligence in two
circumstances;
AI Tests
AI Tests
1. A machine may well be
Can you tell intelligent without
Applications me what is
222222*67344
?
being able to chat
exactly like a human; and;
Data Pyramid Why
Sir?
2. The test fails to capture
Knowledge the general properties of
Based Systems intelligence, such as the
ability to solve difficult
Pros and Cons The Boss could not judge who was replying, problems or come up with
thus the machine is as intelligent as the original insights. If a
secretary.
Bio-inspired machine can solve a
difficult problem that
The Turing test
Example no person could solve,
it would, in principle, fail
Acknowledgement the test.
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7. Artificial Intelligence
Can you find any test to check the given system is intelligent or not?
Introduction
Walks,
AI Tests
AI Tests Makes and perceives, tests,
understands joke smells, and
feels like
Applications human
Reacts
differently
Data Pyramid
Solves your
Knowledge problem
If it talks
Based Systems like human
Pros and Cons
Bio-inspired
Translates,
conceptually form a test summarizes,
Example and use it in different situation and learns
before accepting it.
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8. Artificial Intelligence
Rich & Knight (1991) classified and described the different areas that
Introduction
Artificial Intelligence techniques have been applied to as follows:
AI Tests
Applications
Applications Mundane Tasks Expert Tasks
• Perception - vision and • Engineering - design, fault
Data Pyramid speech finding, manufacturing
• Natural language planning, etc.
Knowledge understanding, generation, • Scientific analysis
Based Systems and translation
• Medical diagnosis
• Commonsense reasoning
Pros and Cons • Financial analysis
• Robot control Formal Tasks
• Games - chess,
Bio-inspired backgammon, checkers, etc.
• Mathematics- geometry,
Example logic, integral calculus,
theorem proving, etc.
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9. Artificial Intelligence
Introduction
IS
AI Tests
Strategy makers apply morals,
WBS Wisdom (experience)
principles, and experience to generate
Applications policies
Higher management generates Knowledge (synthesis)
KBS
knowledge by synthesizing
DataPyramid
Data Pyramid information
Middle management uses reports/info. DSS, MIS Information (analysis)
Knowledge generated though analysis and acts
accordingly
Based Systems
TPS Data (processing of raw observations )
Basic transactions by operational
Pros and Cons staff using data processing
Bio-inspired Volume Sophistication
and complexity
Example
Data pyramid
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10. Artificial Intelligence
Introduction
Knowledge Inference
base engine
Explanation Self-
AI Tests
and learning
reasoning User interface
Applications
Data Pyramid
Knowledge
Knowledge General structure of KBS
Based systems
Based Systems
Pros and Cons
According to the classifications by Tuthhill & Levy (1991), five main types
of KBS exists:
 Expert systems
Bio-inspired
 Linked Systems
 CASE based Systems
Example  Intelligent Tutoring Systems
 Intelligent User Interface for Database
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11. Artificial Intelligence
Introduction Knowledge Sources and Types
AI Tests
Applications
Data Pyramid
Knowledge
Knowledge
Based systems
Based Systems
Pros and Cons
Bio-inspired
Example
Acknowledgement
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12. Artificial Intelligence
Introduction Knowledge Representation
AI Tests
Applications
Data Pyramid
Knowledge
Knowledge
Based systems
Based Systems
Pros and Cons
Bio-inspired
Example
Acknowledgement
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13. Artificial Intelligence
Introduction  Intelligence, explanation and reasoning
 Partial self learning, uncertainty handling
AI Tests  Documentation of knowledge
Applications
 Proactive problem solving
 Cost effectiveness
Data Pyramid
Knowledge
Based Systems  Nature of knowledge
 Large volume of knowledge
Pros and Cons
Pros and Cons
 Knowledge acquisition techniques
Bio-inspired  Little support to engineer AI based systems
 Shelf life of knowledge and system
Example
 Development Effort
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14. Artificial Intelligence
Introduction Bio-Inspired Computing
AI Tests  New approaches to AI
 Taking inspiration form nature and biological systems
Applications  Includes models such as
 Artificial Neural Network (ANN),
Data Pyramid  Genetic Algorithm(GA),
Knowledge  Swarm Intelligence(SI), etc.
Based Systems  Nature has virtues of self learning, evolution,
Pros and Cons
emergence and immunity
 The objective of bio-inspired models and techniques to
Bio-inspired
Bio-inspired take inspiration from Mother Nature and solve
problems in more effective and intelligent way
Example
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15. Artificial Intelligence
Introduction Artificial Neural Network (ANN)
AI Tests  An artificial neural network (ANN) is connectionist model of
programming using computers.
Applications  An ANN attempts to give computers humanlike abilities by
mimicking the human brain’s functionality.
Data Pyramid  The human brain consists of a network of more than a hundred
billions interconnected neurons working in a parallel fashion.
Knowledge
Based Systems W1
X1
Pros and Cons
X2 W2 XiWi y
… ….
Bio-inspired
Bio-inspired W
n
Xn
Example A biological neuron An artificial neuron
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16. Artificial Intelligence
Introduction
AI Tests
Applications
Data Pyramid
Input layer Hidden layers
Knowledge X1 W12 Output layer
Based Systems
X2
O0
Pros and Cons X3 . .
. O1
. . . .
. . . . ….
Bio-inspired
Bio-inspired . . . . Om
.
Example Xn
W1h
A multilayer perceptron
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17. Artificial Intelligence
Introduction Genetic Algorithms (GA)
• It mimics Nature’s evolutionary approach
AI Tests • The algorithm is based on the process of natural selection—
Charles Darwin’s “survival of the fittest.”
Applications
• GAs can be used in problem solving, function optimizing,
machine learning, and in innovative systems.
Data Pyramid Start with initial population
by randomly selected Initial population
Individuals
Knowledge
Modify
Based Systems with Selection Crossover Mutation
operations
Pros and Cons
Evaluate
fitness of new Evaluating new individuals through fitness function
Bio-inspired
Bio-inspired individuals
Update population with
better individuals and Modify the population
Example repeat
Genetic cycle
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18. Artificial Intelligence
Introduction Swarm Intelligence
AI Tests  Inspired by the collective behavior of social insect
colonies and other animal societies
Applications  Ant colony, fish school, bird flocking and honey comb
are the examples
Data Pyramid
Knowledge
Based Systems
Pros and Cons
Bio-inspired
Bio-inspired
Example
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19. Artificial Intelligence
Introduction Some more examples ….
AI Tests
Applications
Data Pyramid
Knowledge
Based Systems
Pros and Cons
Bio-inspired
Bio-inspired
Example
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20. Artificial Intelligence
Introduction
AI Tests Implicit and
self learning
Fuzzy Interface
by ANN
Applications
Data Pyramid
Knowledge
Underlying
Based Systems Users choice Fuzzy interface ANN
and needs
Linguistic Fuzzy rule base Crisp P
1
Pros and Cons
P
fuzzy and membership Normalized
2
P
3
P
interface functions values
4
Rulebase
Bio-inspired Decision
support Decision
support
Structure of proposed system
Example
Acknowledgement
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Created By Priti Srinivas Sajja

21. Artificial Intelligence
Introduction
AI Tests
Applications
Data Pyramid
Knowledge
Based Systems
Pros and Cons
Bio-inspired
Example
Example
Acknowledgement
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22. Artificial Intelligence
Introduction References
 llustrationsOf.com
AI Tests  www.gadgetcage.com
 Engadget.com
 scenicreflections.com
Applications  lih.univ-lehavre.fr
 business2press.com
Data Pyramid  globalswarminghoneybees.blogspot.com
 Knowledge-based systems, Akerkar RA and Priti Srinivas Sajja, Jones & Bartlett
Publishers, Sudbury, MA, USA (2009)
Knowledge
Based Systems
Pros and Cons
Bio-inspired
Example
Acknowledgement
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