artificial intelligence priti srinivas sajja professor department of computer science sardar patel...

Artificial Intelligence

Priti Srinivas SajjaProfessor

Department of Computer ScienceSardar Patel University

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1Created by Priti Srinivas Sajja


AI Tests

Applications

Data Pyramid

Knowledge Based Systems

Pros and Cons

Bio-inspired

Example of Neuro-fuzzy

Acknowledgement

Introduction


• 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


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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


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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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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


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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


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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


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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


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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 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


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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


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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.


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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


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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


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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



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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


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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.



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Knowledge Update

Update by knowledge engineer

Self-update by system Update by expert through interface



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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.


Example


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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 …..*/


Example


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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,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.


Example


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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.


Example


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Introduction


• 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

http://en.wikipedia.org/wiki/Chatterbot


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// 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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http://easydiagnosis.com/


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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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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


Bio-inspired


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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.


Bio-inspired

A biological neuron An artificial neuron

X2

X1

XiWi

W1

W2

… ….

y

Xn

Wn


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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


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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.


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


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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


Bio-inspired


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Introduction Some more examples ….


Natural

Inspired

Bio-inspired


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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


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Example


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36

This slideshow is available here


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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

http://www.jbpub.com/Catalog/9780763776473/Overview/

artificial intelligence priti srinivas sajja professor department of computer science sardar patel...

Documents

intelligence knowledge

knowledgebased systems

fuzzy example of neuro

priti srinivas sajja

introduction slide

national conferences

computer science thesis

better elaine rich