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

2 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 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

3 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 3 Created 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

4 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 4 Created 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

5 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 5 Created by Priti Srinivas Sajja 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

6 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 6 Created by Priti Srinivas Sajja Artificial intelligence Introduction Extreme solution, either best or worst taking  (infinite) time time Acceptable solution in acceptable time Nature of AI solutions

7 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 7 Created by Priti Srinivas Sajja Testing Intelligence AI 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 *67344 ? Why Sir? The Boss could not judge who was replying, thus the machine is as intelligent as the secretary. The Turing test

8 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 8 Created by Priti Srinivas Sajja 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 Makes and understands joke

9 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 9 Created 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

10 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 10 Created by Priti Srinivas Sajja 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 ) VolumeSophistication and complexity TPS DSS, MIS KBS WBS IS Data pyramid Data Pyramid

11 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 11 Created by Priti Srinivas Sajja 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 Explanation and reasoning Self- learning Self- learning General structure of KBS Knowledge Based Systems Knowledge Based Systems Knowledge-Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.

12 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 12 Created by Priti Srinivas Sajja Knowledge Based Systems 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 Types of Knowledge Tacit knowledge Explicit knowledge Commonsense knowledge Informed commonsense knowledge Heuristic knowledge Domain knowledge Meta knowledge

13 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 13 Created by Priti Srinivas Sajja Problem Solving Talking and Story Telling Supervisory Style Dealing with Multiple Experts Knowledge Engineer Hierarchical handling Group handling Individual expert handling Knowledge Acquisition Knowledge Acquisition

14 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 14 Created by Priti Srinivas Sajja KBS requirements Knowledge 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 CONCEPTULIZATION FORMALIZATION IMPLEMENTATION 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 Knowledge Based Systems

15 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 15 Created by Priti Srinivas Sajja Knowledge Based Systems 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 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 Name: Power Bike Broad Category:Land Vehicle Sub Category:Gearless Fuel Type:Gas Cost:$ 350 Capacity:Two persons Speed: 160 Km/Hour Frame

16 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 16 Created by Priti Srinivas Sajja Knowledge Representation Script Name: Visit to Pharmacy Props:Money Symptoms Treatment Medicine Roles:Dentist - D Receptionist - R Patient - P 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. 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 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 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. Scene 3: Exiting P pays money to R. P exits the pharmacy. Knowledge Based Systems Knowledge Based Systems

17 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 17 Created by Priti Srinivas Sajja Knowledge Update Update by knowledge engineer Self-update by system Update by expert through interface Knowledge Based Systems Knowledge Based Systems

18 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 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. 18 Created by Priti Srinivas Sajja Example

19 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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 …..*/ 19 Created by Priti Srinivas Sajja Example

20 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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. 20 Created by Priti Srinivas Sajja Example

21 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 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. 21 Created by Priti Srinivas Sajja Example

22 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 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 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.chatterbots Example

23 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 23 Created by Priti Srinivas Sajja // Description: this is a very basic example of a chatterbot program by Gonzales Cenelia #include 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

24 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 24 Created by Priti Srinivas Sajja

25 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 25 Created by Priti Srinivas Sajja

26 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 26 Created by Priti Srinivas Sajja

27 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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 27 Created by Priti Srinivas Sajja Pros and Cons

28 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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 28 Created by Priti Srinivas Sajja Bio-inspired

29 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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. 29 Created by Priti Srinivas Sajja Bio-inspired A biological neuron An artificial neuron X2X2 X1X1 XiWiXiWi W1W1 W2W2 … …. y XnXn WnWn

30 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 30 Created by Priti Srinivas Sajja Bio-inspired W 12 X1X2X3....XnX1X2X3....Xn O 0 O 1 …. O m W 1h Input layerHidden layers Output layer A multilayer perceptron Mom (0.3) Dad (0.5) ∑W i X i Going to Army: to Be or not to Be? Importance to Mom Importance to Dad = 0.3* *0.4 = = 0.38 which is < 0.6 = 0.3* *0.4 = = 0.38 which is < 0.6

31 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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. 31 Created by Priti Srinivas Sajja Genetic cycle Modify with operations Modify with operations Start with initial population by randomly selected Individuals Start with initial population by randomly selected Individuals Evaluate fitness of new individuals Evaluate fitness of new individuals Update population with better individuals and repeat Update population with better individuals and repeat Initial population Selection Mutation Crossover Evaluating new individuals through fitness function Modify the population Bio-inspired

32 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy 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 32 Created by Priti Srinivas Sajja Bio-inspired

33 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction Some more examples …. 33 Created by Priti Srinivas Sajja Bio-inspired

34 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 34 Created by Priti Srinivas Sajja Fuzzy Interface 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 Decision support Underlying ANN P1P2P3P4P1P2P3P4 Implicit and self learning by ANN Friendly interface, Explicit justification, and Documentation Friendly interface, Explicit justification, and Documentation Example

35 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 35 Created by Priti Srinivas Sajja Example

36 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 36 This slideshow is available here

37 Artificial Intelligence AI Tests Applications Data Pyramid Knowledge Based Systems Knowledge Based Systems Pros and Cons Bio-inspired Example of Neuro-fuzzy Example of Neuro-fuzzy Acknowledgement Introduction 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) Knowledge-based systems Acknowledgement


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