Artificial Intelligence
Priti Srinivas SajjaProfessor
Department of Computer ScienceSardar Patel University
Visit pritisajja.info for details
1Created by Priti Srinivas Sajja
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
2Created by Priti Srinivas Sajja
• Name: Dr. Priti Srinivas Sajja• Communication:
• Email : [email protected]• Mobile : +91 9824926020• URL :http://pritisajja.info
• Academic qualifications : Ph. D in Computer Science
• Thesis title: Knowledge-Based Systems for Socio- • Economic Rural Development (2000)• Subject area of specialization : Artificial Intelligence
• Publications : 118 in Books, Book Chapters, Journals and in Proceedings of International and National Conferences
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
3Created by Priti Srinivas Sajja
Natural intelligence Responds to situations flexibly. Makes sense of ambiguous or erroneous messages. Assigns relative importance to elements of a
situation. Finds similarities even though the situations might
be different. Draws distinctions between situations even though
there may be many similarities between them.
Introduction
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
4Created by Priti Srinivas Sajja
Introduction
“Artificial Intelligence(AI) is the study of how to make computers do things at which,
at the moment, people are better”
• Elaine Rich, Artificial Intelligence, McGraw Hill Publications, 1986
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Artificial intelligence Introduction
where people are better
human thought process
characteristics we associate with intelligence
knowledge using symbols
heuristic methods
non-algorithmic
Constituents of artificial intelligence
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Artificial intelligence Introduction
Extreme solution, either best or worst taking (infinite) time
time
Acceptable solution in acceptable time
Nature of AI solutions
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Testing IntelligenceAI Tests
Turing test will fail to test for intelligence in two circumstances;
1. A machine may well be intelligent without being able to chat exactly like a human; and;
2. The test fails to capture the general properties of intelligence, such as the ability to solve difficult problems or come up with original insights. If a machine can solve a difficult problem that no person could solve, it would, in principle, fail the test.
Can you tell me what is
222222*67344?
Why Sir?
The Boss could not judge who was replying, thus the machine is as intelligent as the secretary.
The Turing test
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Can you find any test to check the given system is intelligent or not?
AI Tests
If it talks like human
Translates, summarizes, and learns
Solves your problem
Reacts differently
Walks, perceives, tests,
smells, and feels like human
conceptually form a test and use it in different situationbefore accepting it.
Makes and understands joke
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
9Created by Priti Srinivas Sajja
Rich & Knight (1991) classified and described the different areas that Artificial Intelligence techniques have been applied to as follows:
Applications Mundane Tasks• Perception - vision and
speech• Natural language
understanding, generation, and translation
• Commonsense reasoning• Robot control Formal Tasks
• Games - chess, backgammon, checkers, etc.
• Mathematics- geometry, logic, integral calculus, theorem proving, etc.
Expert Tasks• Engineering - design, fault
finding, manufacturing planning, etc.
• Scientific analysis • Medical diagnosis • Financial analysis
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Basic transactions by operational staff using data processing
Middle management uses reports/info. generated though analysis and acts accordingly
Higher management generates knowledge by synthesizing information
Strategy makers apply morals, principles, and experience to generate policies
Wisdom (experience)
Knowledge (synthesis)
Information (analysis)
Data (processing of raw observations )
Volume Sophistication and complexity
TPS
DSS, MIS
KBS
WBS
IS
Data pyramid
Data Pyramid
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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According to the classifications by Tuthhill & Levy (1991), five main types of KBS exists:
Expert systems Linked Systems CASE based Systems Intelligent Tutoring Systems Intelligent User Interface for Database
Knowledge base
Inference engine
User interface
Explanation and
reasoning
Self-learning
General structure of KBS
Knowledge Based Systems Knowledge-Based Systems (KBS) are Productive Artificial Intelligence Tools
working in a narrow domain.
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Knowledge Based Systems
Experience
Satellite Broadcasting (Internet, TV,
and Radio)Printed Media
Experts
Sources of knowledge
Types of Knowledge • Tacit knowledge
• Explicit knowledge
• Commonsense knowledge
• Informed commonsense knowledge
• Heuristic knowledge
• Domain knowledge
• Meta knowledge
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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• Problem Solving
• Talking and Story Telling
• Supervisory Style
• Dealing with Multiple Experts
Knowledge Engineer
Hierarchical handling
Group handlingIndividual
expert handling
Knowledge Acquisition
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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KBS requirementsKnowledge
Engineer
Data Base
Cases and documents
Knowledge Base
Knowledge Acquisition Techniques
• Literature review• Protocol analysis• Diagram-based techniques• Concept sorting• etc.
Experts
Other Knowledge
Sources
User
Automatic creation from cases
Knowledge discovery and
verification
Activities in the knowledge acquisition process
IDENTIFICATION
IDENTIFICATION
CONCEPTULIZATION
FORMALIZATIONIMPLEMENTATION
TESTING
Knowledge representation
• Find suitable experts and a knowledge engineer• Proper homework and planning• Interpreting and understanding the knowledge provided by the experts• Representing the knowledge provided by the experts
Knowledge Based Systems
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Knowledge Based Systems
Knowledge Representation
Constant: RAM, LAXMAN
Variable: Man
Function: Elder (RAM, LAXMAN) returns any value, here, RAM
Predicate: Mortal (RAM) returns a Boolean value, here, True
WFF: ‘If you do not exercise, you will gain weight is represented as: x[{Human(x) ^ ~Exercise (x)} Gain weight(x)]
Factual Knowledge Representation
Person
Doctor Patient
Medicine
Give
Instance Instance
Agent Recipient
Semantic Network
Name: Power Bike
Broad Category: Land Vehicle
Sub Category: Gearless
Fuel Type:Gas
Cost:$ 350
Capacity:Two persons
Speed: 160 Km/Hour
Frame
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Knowledge Representation
Script
Name: Visit to Pharmacy
Props: MoneySymptomsTreatmentMedicine
Roles: Dentist - DReceptionist - RPatient - P
Entry Conditions:Patient P has toothache. Patient P has money.
Exit ConditionsPatient P has less money.Patient P returns with treatment. Patient P has appointment.Patient P has prescription.
Scene 1: EntryP enters to the pharmacy.P goes to reception. P meets R.P pays registration and/or fees and gets appointment. Go to Scene 2.
Scene 2: Consulting DoctorP meets D.P conveys symptoms.
P gets treatment. P gets appointment.
Go to Scene 3.
Scene 3: ExitingP pays money to R.P exits the pharmacy.
Knowledge Based Systems
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Knowledge Update
Update by knowledge engineer
Self-update by system Update by expert through interface
Knowledge Based Systems
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction domainsdisease, indication=symbol patient=string
predicateshypothesis(patient, disease)symptom(patient, indication)response(char)go
clausesgo:-write("What is the patient's name?"), nl,readln(Patient),hypothesis(Patient, Disease),write(Patient," probably has ",Disease,"."), nl.
go:-write("Sorry, I don't seen to be able to "), nl,write("diagnose the disease."), nl.
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Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction symptom(Patient, fever):-write("Does ",Patient," have a fever (y/n)?"),response(Reply), Reply='y'.
symptom(Patient, rash):-write("Dose ",Patient," have a rash (y/n)?"),response(Reply), Reply='y'.
symptom(Patient, headache):-write("Dose ",Patient," have a headache (y/n)?"),response(Reply), Reply='y'.
symptom(Patient, runny_nose):-write("Dose ",Patient," have a runny nose (y/n)?"),response(Reply), Reply='y'.
symptom(Patient, conjunctivities):-write("Dose ",Patient," have conjunctivities (y/n)?"),response(Reply), Reply='y'.
symptom(Patient, cough):-write("Dose ",Patient," have a cough (y/n)?"),response(Reply), Reply='y'.
/*… you may write number of symptoms here …..*/
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Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
hypothesis(Patient,measles):-symptom(Patient,fever),symptom(Patient,cough),symptom(Patient,conjunctivities),symptom(Patient,runny_nose),symptom(Patient,rash).
hypothesis(Patient, german_measles):-
symptom(Patient,fever),symptom(Patient,headache),symptom(Patient,runny_nose),symptom(Patient,rash).
hypothesis(Patient, flu):-symptom(Patient,fever),symptom(Patient,headache),symptom(Patient,body_ache),symptom(Patient,conjunctivities),symptom(Patient,chills),symptom(Patient,sore_throat),symptom(Patient,cough),symptom(Patient,runny_nose). /* number of hypothesis you may include here….*/ response(Reply):- readchar(Reply), write(Reply), nl.
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Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction OUTPUT IN DIALOG BOX:Goal: go
What is the Patient’s Name? BondDoes Bond Have a fever (y/n)? yDoes Bond Have a cough (y/n)? yDoes Bond Have conjunctivities (y/n)? yDoes Bond Have a rash (y/n)? yDoes Bond Have a runny nose (y/n)? y
Bond probably has measles.
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Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
22Created by Priti Srinivas Sajja
• ELIZA is a computer program and an early example of primitive natural language processing.
• ELIZA was written at MIT by Joseph Weizenbaum between 1964 to 1966.
• ELIZA was implemented using simple pattern matching techniques, but was taken seriously by several of its users, even after Weizenbaum explained to them how it worked.
• It was one of the first chatterbots in existence.
Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
23Created by Priti Srinivas Sajja
// Description: this is a very basic example of a chatterbot program by Gonzales Cenelia #include <iostream> #include <string> #include <ctime> int main() {
std::string Response[] = {"I HEARD YOU!", "SO, YOU ARE TALKING TO ME.", CONTINUE, I AM LISTENING.", "VERY INTERESTING CONVERSATION.", "TELL ME MORE..." }; srand((unsigned) time(NULL)); std::string sInput = ""; std::string sResponse = ""; while(1)
{ std::cout << ">"; std::getline(std::cin, sInput); int nSelection = rand() % 5; sResponse = Response[nSelection]; std::cout << sResponse << std::endl; }
return 0; }
Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
24Created by Priti Srinivas Sajja
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
25Created by Priti Srinivas Sajja
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
26Created by Priti Srinivas Sajja
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Intelligence, explanation and reasoning Partial self learning, uncertainty handling Documentation of knowledge Proactive problem solving Cost effectiveness
Nature of knowledge Large volume of knowledge
Knowledge acquisition techniques Little support to engineer AI based systems
Shelf life of knowledge and system Development Effort
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Pros and Cons
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Bio-Inspired Computing New approaches to AI Taking inspiration form nature and biological systems Includes models such as
Artificial Neural Network (ANN) Genetic Algorithm(GA) Swarm Intelligence(SI), etc.
Nature has virtues of self learning, evolution, emergence and immunity
The objective of bio-inspired models and techniques to take inspiration from Mother Nature and solve problems in more effective and intelligent way
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Bio-inspired
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Artificial Neural Network (ANN) An artificial neural network (ANN) is connectionist model of
programming using computers. An ANN attempts to give computers humanlike abilities by
mimicking the human brain’s functionality. The human brain consists of a network of more than a hundred
billions interconnected neurons working in a parallel fashion.
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Bio-inspired
A biological neuron An artificial neuron
X2
X1
XiWi
W1
W2
… ….
y
Xn
Wn
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Bio-inspired
W12X1
X2
X3
.
.
.
.
Xn
O0
O1
….Om
.
.
.
.
.
.
.
.
.
.
.
.
W1h
Input layer Hidden layers
Output layer
A multilayer perceptron
Mom
(0.3)
Dad
(0.5)
∑Wi Xi0.60.6
0.4
Going to Arm
y:
to Be or not to
Be? Importance to Mom
Importance to Dad
= 0.3*0.6 + 0.5*0.4 = 0.18 +0.20= 0.38 which is < 0.6
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Genetic Algorithms (GA)• It mimics Nature’s evolutionary approach • The algorithm is based on the process of natural selection—
Charles Darwin’s “survival of the fittest.” • GAs can be used in problem solving, function optimizing,
machine learning, and in innovative systems.
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Genetic cycle
Modify withoperations
Start with initial population by randomly selected Individuals
Evaluate fitness of newindividuals
Update population with better individuals and repeat
Initial population
Selection MutationCrossover
Evaluating new individuals through fitness function
Modify the population
Bio-inspired
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Swarm Intelligence Inspired by the collective behavior of social insect
colonies and other animal societies Ant colony, fish school, bird flocking and honey comb
are the examples
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Bio-inspired
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction Some more examples ….
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Natural
Inspired
Bio-inspired
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Fu
zzy Interface
Structure of proposed system
Fuzzy interfaceLinguistic
fuzzy interface
Fuzzy rule base and membership functions
Workspace
Crisp Normalized
values
Decision support
Users choice and needs
Decision support
Underlying ANN
P1
P2
P3
P4
Implicit and self learning
by ANN
Friendly interface,
Explicit justification,
and Documentation
Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
35Created by Priti Srinivas Sajja
Example
Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
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Artificial Intelligence
AI Tests
Applications
Data Pyramid
Knowledge Based Systems
Pros and Cons
Bio-inspired
Example of Neuro-fuzzy
Acknowledgement
Introduction
37Created by Priti Srinivas Sajja
References llustrationsOf.com www.gadgetcage.com Prersentermedia.com Presentationmagazine.com Clikr.com Engadget.com scenicreflections.com lih.univ-lehavre.fr business2press.com globalswarminghoneybees.blogspot.com Knowledge-based systems, Akerkar RA and Priti Srinivas Sajja, Jones & Bartlett Publishers,
Sudbury, MA, USA (2009)
Acknowledgement