1. Artificial Intelligence
Priti Srinivas Sajja
Professor
Department of Computer Science
Sardar Patel University
Visit pritisajja.info for details H
Created by Priti Srinivas Sajja

2. Created by Priti Srinivas Sajja
Name: Dr. Priti Srinivas Sajja
Communication:
Mobile :
URL :
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
Created by Priti Srinivas Sajja

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Introduction
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.
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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
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5. AI Artificial intelligence Introduction human thought process
where people are better
human thought process
characteristics we associate with intelligence
knowledge using symbols
heuristic methods
non-algorithmic AI
Constituents of artificial intelligence
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Introduction
Artificial intelligence
Extreme solution, either best or worst taking  (infinite) time
time
Acceptable solution in acceptable time
Nature of AI solutions
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Testing Intelligence
Turing test will fail to test for intelligence in two circumstances;
A machine may well be intelligent without being able to chat exactly like a human; and;
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.
AI Tests
Can you tell me what is *67344?
Why Sir?
The Boss could not judge who was replying, thus the machine is as intelligent as the secretary.
The Turing test
Created by Priti Srinivas Sajja

8. conceptually form a test and use it in different situation
Can you find any test to check the given system is intelligent or not?
Walks, perceives, tests, smells, and feels like human
AI Tests
Makes and understands joke
Reacts differently
Solves your problem
If it talks like human
Translates, summarizes, and learns
conceptually form a test
and use it in different situation
before accepting it.
Created by Priti Srinivas Sajja

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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
Expert Tasks
Engineering - design, fault finding, manufacturing planning, etc.
Scientific analysis
Medical diagnosis
Financial analysis
Formal Tasks
Games - chess, backgammon, checkers, etc.
Mathematics- geometry, logic, integral calculus, theorem proving, etc.
Created by Priti Srinivas Sajja

10. Sophistication and complexity
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
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11. General structure of KBS
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 (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.
Knowledge Based Systems
Created by Priti Srinivas Sajja

12. Satellite Broadcasting (Internet, TV, and Radio)
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
Knowledge Based Systems
Created by Priti Srinivas Sajja

13. Hierarchical handling Individual expert handling
Problem Solving
Talking and Story Telling
Supervisory Style
Dealing with Multiple Experts
Knowledge Acquisition
Knowledge Engineer
Hierarchical handling
Group handling
Individual expert handling
Created by Priti Srinivas Sajja

14. Other Knowledge Sources
IDENTIFICATION
Experts
Other Knowledge Sources
CONCEPTULIZATION
IDENTIFICATION
Knowledge Acquisition Techniques
Literature review
Protocol analysis
Diagram-based techniques
Concept sorting
etc.
KBS requirements
Knowledge Engineer
User
Knowledge representation
Knowledge discovery and verification
IMPLEMENTATION
FORMALIZATION
Knowledge Base
Knowledge Based Systems
Data Base
Cases and documents
Automatic creation from cases
TESTING
Activities in the knowledge acquisition process
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
Created by Priti Srinivas Sajja

15. 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
Knowledge Based Systems
Name: Power Bike
Broad Category: Land Vehicle
Sub Category: Gearless
Fuel Type: Gas
Cost: $ 350
Capacity: Two persons
Speed: Km/Hour
Person
Doctor
Patient
Medicine
Give
Instance
Agent
Recipient
Semantic Network
Frame
Created by Priti Srinivas Sajja

16. Knowledge Representation Created by Priti Srinivas Sajja
Name: Visit to Pharmacy
Props: Money
Symptoms
Treatment
Medicine
Roles: Dentist - D
Receptionist - R
Patient - P
Entry Conditions:
Patient P has toothache.
Patient P has money.
Exit Conditions
Patient P has less money.
Patient P returns with treatment.
Patient P has appointment.
Patient P has prescription.
Scene 1: Entry
P 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 Doctor
P meets D.
P conveys symptoms.
P gets treatment. P gets appointment.
Go to Scene 3.
Scene 3: Exiting
P pays money to R.
P exits the pharmacy.
Knowledge Based Systems
Script
Created by Priti Srinivas Sajja

17. Update by knowledge engineer Update by expert through interface
Knowledge Update
Update by knowledge engineer
Self-update by system
Update by expert through interface
Knowledge Based Systems
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domains
disease, indication=symbol
patient=string
predicates
hypothesis(patient, disease)
symptom(patient, indication)
response(char) go
clauses
go:-
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.
Example
Created by Priti Srinivas Sajja

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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)?"),
symptom(Patient, headache):-
write("Dose ",Patient," have a headache (y/n)?"),
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)?"),
symptom(Patient, cough):-
write("Dose ",Patient," have a cough (y/n)?"),
/*… you may write number of symptoms here …..*/
Example
Created by Priti Srinivas Sajja

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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,headache),
hypothesis(Patient, flu):-
symptom(Patient,body_ache),
symptom(Patient,chills),
symptom(Patient,sore_throat),
symptom(Patient,runny_nose).
 /* number of hypothesis you may include here….*/
 response(Reply):- readchar(Reply), write(Reply), nl.
Example
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21. Created by Priti Srinivas Sajja
OUTPUT IN DIALOG BOX:
Goal: go
What is the Patient’s Name? Bond
Does Bond Have a fever (y/n)? y
Does Bond Have a cough (y/n)? y
Does Bond Have conjunctivities (y/n)? y
Does Bond Have a rash (y/n)? y
Does Bond Have a runny nose (y/n)? y
Bond probably has measles.
Example
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22. Created 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
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23. Created 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
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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
Pros and Cons
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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
Bio-inspired
Created by Priti Srinivas Sajja

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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.
An artificial neuron X2 X1
XiWi W1 W2
… …. y Xn Wn
A biological neuron
Bio-inspired
Created by Priti Srinivas Sajja

30. Going to Army: to Be or not to Be? A multilayer perceptron
Mom
(0.3)
Dad
(0.5)
∑Wi Xi
0.6
0.4
Going to Army: to Be or not to Be?
Importance to Mom
Importance to Dad
= 0.3* *0.4 =
= which is < 0.6
W12 X1 X2 X3 . Xn O0 O1 …. Om
W1h
Input layer Hidden layers
Output layer
A multilayer perceptron
Bio-inspired
Created by Priti Srinivas Sajja

31. Evaluating new individuals through fitness function
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.
Genetic cycle
Modify
with
operations
Start with initial population
by randomly selected
Individuals
Evaluate
fitness of new
individuals
Update population with
better individuals and
repeat
Initial population
Selection
Mutation
Crossover
Evaluating new individuals through fitness function
Modify the population
Bio-inspired
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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
Bio-inspired
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Some more examples ….
Natural
Inspired
Bio-inspired
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34. Example Fuzzy Interface Created by Priti Srinivas Sajja
Structure of proposed system
Fuzzy interface
Linguistic fuzzy interface
Fuzzy rule base and membership functions
Workspace
Crisp Normalized values
Decision support
Users choice and needs
Underlying ANN P1 P2 P3 P4
Implicit and self learning by ANN
Friendly interface,
Explicit justification, and Documentation
Example
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Example
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36. This slideshow is available here

37. Created by Priti Srinivas Sajja
References
llustrationsOf.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
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