Introduction to Artificial Intelligence

1. CPC703
ARTIFICIAL
INTELLIGENCE

2. Objectives
1. To conceptualize the basic ideas and
techniques underlying the design of intelligent
systems.
2. To make students understand and explore the
mechanism of mind that enable intelligent
thought and action.
3. To make students understand advanced
representation formalism and search
techniques.
4. To make students understand how to deal with
uncertain and incomplete information.shiwani gupta 2

3. Outcomes
Learner will be able to
1. develop a basic understanding of AI building blocks
presented in intelligent agents.
2. choose an appropriate problem solving method and
knowledge representation technique.
3. analyze the strength and weaknesses of AI
approaches to knowledge – intensive problem
solving.
4. design models for reasoning with uncertainty as well
as the use of unreliable information.
5. design and develop the AI applications in real world
scenario.
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4. Learning Outcomes
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At the end of this course, learner should be able to:
• Knowledge and understanding
know and understand the basic concepts of Artificial
Intelligence including Search, Game Playing, KBS (including
Uncertainty), Planning and Machine Learning.
• Intellectual skills
use this knowledge and understanding of appropriate principles
and guidelines to synthesize solutions to tasks in AI and to
critically evaluate alternatives.
• Practical skills
use a well known declarative language (Prolog) and to
construct simple AI systems.
• Transferable Skills
solve problems and evaluate outcomes and alternatives.

5. List of AI Practical / Experiments
All the programs should be implemented in C/C++/Java/Prolog
under Windows or Linux environment. Experiments can also be
conducted using available open source tools.
1. One case study on NLP/Expert system based papers
published in IEEE/ACM/Springer or any prominent journal.
2. Program on uninformed and informed search methods.
3. Program on Local Search Algorithm.
4. Program on Optimization problem.
5. Program on adversarial search.
6. Program on Wumpus world.
7. Program on unification.
8. Program on Decision Tree.
Any other practical covering the syllabus topics and subtopics can
be conducted.shiwani gupta 5

6. REFERENCE BOOKS (Practicals)
1. Ivan Bratko "PROLOG Programming for Artificial Intelligence",
Pearson Education, Third Edition.
2. Elaine Rich and Kevin Knight "Artificial Intelligence "Third
Edition
3. Davis E.Goldberg, "Genetic Algorithms: Search, Optimization
and Machine Learning", Addison Wesley, N.Y., 1989.
4. Han Kamber, “Data Mining Concepts and Techniques”,
Morgann Kaufmann Publishers. Text Books:
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7. TEXT BOOKS
1. Stuart J. Russell and Peter Norvig, "Artificial Intelligence A
Modern Approach “Second Edition" Pearson Education.
2. Saroj Kaushik “Artificial Intelligence” , Cengage Learning.
3. George F Luger “Artificial Intelligence” Low Price Edition ,
Pearson Education., Fourth edition.
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8. REFERENCE BOOKS
1. Ivan Bratko “PROLOG Programming for Artificial Intelligence”,
Pearson Education, Third Edition.
2. Elaine Rich and Kevin Knight “Artificial Intelligence” Third
Edition
3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization
and Machine Learning”, Addison Wesley, N.Y., 1989.
4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE
Learning, India Edition.
5. Patrick Henry Winston , “Artificial Intelligence”, Addison-
Wesley, Third Edition.
6. Han Kamber, “Data Mining Concepts and Techniques”,
Morgann Kaufmann Publishers.
7. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”,
Oxford University Press.
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9. Module 1
INTRODUCTION TO AI
(04)

10. • Introduction
• History of Artificial Intelligence
• Intelligent Systems: Categorization of Intelligent
System
• Components of AI Program
• Foundations of AI
• Sub-areas of AI
• Applications of AI
• Current trends in AI
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11. INTRODUCTION
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Can Computers beat Humans at Chess?
Chess Playing is a classic AI problem
– well-defined problem
– very complex: difficult for humans to play well
Human World Champion
Deep Blue
Deep Thought
PointsRatings
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
1966 1971 1976 1981 1986 1991 1997
Ratings

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Can Computers Talk?
• This is known as “speech synthesis”
– translate text to phonetic form
• e.g., “fictitious” -> fik-tish-es
– use pronunciation rules to map phonemes to actual sound
• e.g., “tish” -> sequence of basic audio sounds
• Difficulties
– sounds made by this “lookup” approach sound unnatural
– sounds are not independent
• e.g., “act” and “action”
• modern systems can handle this pretty well
– a harder problem is emphasis, emotion, etc
• humans understand what they are saying
• machines don’t; so they sound unnatural
• Conclusion:
– NO, for complete sentences
– YES, for individual words

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Can Computers Recognize Speech?
• Speech Recognition:
– mapping sounds from a microphone into a list of words
eg. A deaf human
• Recognizing single words from a small vocabulary
• systems can do this with high accuracy (order of 99%)
• e.g., directory inquiries
– limited vocabulary (area codes, city names)
– computer tries to recognize you first, if unsuccessful hands
you over to a human operator
– saves millions of dollars a year for the telephone companies

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• Recognizing normal speech is much more difficult
– speech is continuous: where are the boundaries between words?
• e.g., “John’s car has a flat tire”
– large vocabularies
• can be many thousands of possible words
• we can use context to help figure out what someone said
e.g., hypothesize and test
– try telling a waiter in a restaurant:
“I would like some sugar in my coffee”
– background noise, other speakers, accents, colds, etc
– on normal speech, modern systems are only about 60-70%
accurate
• Conclusion:
– NO, normal speech is too complex to accurately recognize
– YES, for restricted problems (small vocabulary, single speaker)

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Can Computers Understand speech?
• Understanding is different to recognition:
– “Where is the water?”
• assume the computer can recognize all the words
• how many different interpretations are there?
– 1. in chemistry lab, it must be pure
– 2. when you are thirsty, it must be potable
– 3. dealing with a leaky roof, it can be filthy
but how could a computer figure this out?
– clearly humans use a lot of implicit commonsense
knowledge in communication
• Conclusion: NO, much of what we say is beyond the capabilities of a
computer to understand at present

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Can Computers Learn and Adapt ?
• Learning and Adaptation
– consider a computer learning to drive on the freeway
– we could teach it lots of rules about what to do and what not to do
– or we could let it drive and steer it back on course when it heads for the
embankment
• systems like this are under development (e.g., Daimler Benz)
• e.g., RALPH at CMU
– in mid 90’s it drove 98% of the way from Pittsburgh to San Diego
without any human assistance
– machine learning allows computers to learn to do things without explicit
programming
– many successful applications require some “set-up”: does not mean your
PC can learn to forecast the stock market or become a brain surgeon
• Conclusion: YES, computers can learn and adapt, when presented with
information in the appropriate way

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• Recognition vs Understanding (like Speech)
– Recognition and Understanding of Objects in a scene
• look around this room
• you can effortlessly recognize objects
• human brain can map 2d visual image to 3d “map”
• Why is visual recognition a hard problem?
Conclusion:
– mostly NO: computers can only “see” certain types of objects
under limited circumstances
– YES for certain constrained problems (e.g. face recognition)
Can Computers “see”?

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Can computers plan and make optimal
decisions?• Intelligence
– involves solving problems and making decisions and plans
– e.g. you want to take a holiday in Brazil
• you need to decide on dates, flights
• you need to get to the airport, etc
• involves a sequence of decisions, plans, and actions
• What makes planning hard?
– the world is not predictable:
• your flight is canceled or there’s a backup
– there are a potentially huge number of details
• do you consider all flights? all dates?
– no: commonsense constrains your solutions
– AI systems are only successful in constrained planning problems
• Conclusion: NO, real-world planning and decision-making is still beyond the
capabilities of modern computers
exception: very well-defined, constrained problems

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Summary of State of AI Systems in Practice
• Speech synthesis, recognition and understanding
– very useful for limited vocabulary applications
– unconstrained speech understanding is still too hard
• Computer vision
– works for constrained problems (hand-written zip-codes)
– understanding real-world, natural scenes is still too hard
• Learning
– adaptive systems are used in many applications: have their limits
• Planning and Reasoning
– only works for constrained problems: e.g., chess
– real-world is too complex for general systems
• Overall
– many components of intelligent systems are “doable”
– there are many interesting research problems remaining

21. HISTORY OF ARTIFICIAL
INTELLIGENCE
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Evolution / History of AI
• The gestation of artificial intelligence (1943-1956)
• Early enthusiasm, great expectations (1952-1969)
• A dose of reality (1966-1974)
• Knowledge-based systems: The key to power? (1969-
1979)
• AI becomes an industry (1980-1988)
• The return of neural networks (1986-present)
• Recent events (1987-present)

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• 1943 Warren McCulloch & Walter Pitts: Boolean circuit model of
brain
• 1949 Donald Hebb discovered how to set connection strengths b/w
neurons
• 1950 Turing's "Computing Machinery and Intelligence“- Turing Test
(the imitation game)
• 1950 Marvin Minsky and Dean Edmonds built first neural net
computer (Snarc)
• 1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry
Engine
• 1952 Arthur Samuel-checkers programs; learned how to improve,
quickly eclipsing Samuel himself
• 1952-69 Look, Ma, no hands! (first toy eg. with lots of enthusiasm)
• 1956 Dartmouth workshop: "Artificial Intelligence" adopted
• 1958 LISP from MIT by John McCarthy
• 1959 Hebert Gelernter (Geometry Theorem Prover)
• 1963 James Slagle-Saint solved basic integration problems.
Evolution / History of AI

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• 1963 McCarthy founds AI lab at Stanford
• 1965 Robinson's complete algorithm for logical reasoning
• 1966-74 AI discovers computational complexity
1966-74 Neural network research almost disappears after Minsky and
Papert’s book in 1969
• 1967 Daniel Bobrow-Student solved algebra story problems
• 1969 DENDRAL by Buchanan et al..
• 1976 MYCIN by Edward Shortliffle in early 1970s.
• 1979 PROSPECTOR by Duda et al..
• 1980-88 Expert systems are a major industry
• 1981 Japan’s 10 year 5th generation project
• Mid 1980s Backpropogation learning algorithm reinvented
• 1985-95 Neural networks resurface connectionism turn to popularity
• 1988- Probability enters into general use
• 1988 Novel AI (ALife, Gas, soft computing,…)
• 1995- The emergence of intelligent agents as part of internet boom
• 2003- Human level AI back as topic of study
Evolution / History of AI

25. Defining AI
The branch of computer science concerned with
making computers behave like humans.
Study of agents that exist in an environment and
perceive and act.
AI strives to build intelligent entities and understand
them.
The term was coined in 1956 by John McCarthy at
the Massachusetts Institute of Technology.

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"The automation of activities that we
associate with human thinking, activities such
as decision-making, problem solving, learning
..."(Bellman, 1978)
"The study of mental faculties through the
use of computational models“ (Charniak and
McDermott, 1985)
"The exciting new effort to make computers
think . . . machines with minds, in the full and
literal sense" (Haugeland, 1985)
"A field of study that seeks to explain and
emulate intelligent behavior in terms of
computational processes" (Schalkoff, 1990)
"The art of creating machines that perform
functions that require intelligence when
performed by people" (Kurzweil, 1990)
"The study of how to make computers do
things at which, at the moment, people are
better" (Rich and Knight, 1991)
"a collection of algorithms that are
computationally tractable, adequate
approximations of intractably specified
problems" (Partridge, 1991)
"the field of computer science that studies
how machines can be made to act
intelligently“ (Jackson, 1986)
"the getting of computers to do things that
seem to be intelligent" (Rowe, 1988).
"a very general investigation of the nature of
intelligence and the principles and
mechanisms required for understanding or
replicating it" (Sharpies et ai, 1989)
"a field of study that encompasses
computational techniques for performing
tasks that apparently require intelligence
when performed by humans" (Tanimoto,
1990)
"The study of the computations that make it
possible to perceive, reason, and act“
(Winston, 1992)
"The branch of computer science that is
concerned with the automation of intelligent
behavior" (Luger and Stubblefield, 1993)
"the enterprise of constructing a physical
symbol system that can reliably pass the
Turing Test" (Ginsberg, 1993)

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Overview of AI
• Artificial intelligence: Computers with the ability to mimic or duplicate
the functions of the human brain
• Artificial intelligence systems: The people, procedures, hardware,
software, data, and knowledge needed to develop computer systems
and machines that demonstrate the characteristics of intelligence
• Intelligent behavior
– Learn from experience
– Apply knowledge acquired from experience
– Handle complex situations
– Solve problems when important information is missing
– Determine what is important
– React quickly and correctly to a new situation
– Understand visual images
– Process and manipulate symbols
– Be creative and imaginative
– Use heuristics

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Major Branches of AI
Perceptive system
• A system that approximates the way a human sees, hears, and feels objects
Vision system
• Capture, store, and manipulate visual images and pictures
Robotics
• Mechanical and computer devices that perform tedious tasks with high
precision
Expert system
• Stores knowledge and makes inferences
Learning system
• Computer changes how it functions or reacts to situations based on feedback
Games playing
• Programming computers to play games such as chess and checkers
Natural language processing
• Computers understand and react to statements and commands made in a
“natural” language, such as English
Neural network
• Computer system that can act like or simulate the functioning of the human
brain

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HAL: from the movie 2001
‘2001: A Space Odyssey” epic science fiction movie in
1968
part of the story centers around an intelligent computer
called HAL 9000
HAL is the “brains” of an intelligent spaceship
in the movie, HAL can
• speak easily with the crew
• see and understand the emotions of the crew
• navigate the ship automatically
• diagnose on-board problems
• make life-and-death decisions
• display emotions
In 1968 this was science fiction: is it still science fiction?

31. AI in Movies
• The Avengers: Age of Ultron (2015)
• Chappie (2015)
• Ex Machina (2015)
• Paul Blart: Mall Cop 2 (2015)
• Ash in Alien (1979)
• Bishop in Aliens (1986)
• Roy Batty in 'Blade Runner' (1982)
• WOPR in 'WarGames' (1983)
• Skynet in 'The Terminator' Series (1984 - 2015)
• Data in 'Star Trek: First Contact' (1996)
• Agent Smith in 'The Matrix' (1999)
• David in 'A.I.: Artificial Intelligence' (2001)
• Gerty in 'Moon' (2009)
• Samantha in 'Her' (2013)
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Most common languages for AI
• LISP- HLL. Features of the language that are good for AI programming include:
garbage collection, dynamic typing, functions as data, uniform syntax, interactive
environment, and extensibility.
• PROLOG- This language wins 'cool idea' competition. It wasn't until the 70s that
people began to realize that a set of logical statements plus a general theorem
prover could make up a program. Prolog combines the high-level and traditional
advantages of Lisp with a built-in unifier, which is particularly useful in AI. Prolog
seems to be good for problems in which logic is intimately involved, or whose
solutions have a succinct logical characterization. Its major drawback is that it's hard
to learn.
• C/C++- The speed demon of the bunch, C/C++ is mostly used when the program is
simple, and execution speed is the most important. Statistical AI techniques such as
neural networks are common examples of this. Back propagation is only a couple of
pages of C/C++ code, and needs every ounce of speed that the programmer can
master.
• Java- The newcomer, Java uses several ideas from Lisp, most notably garbage
collection. Its portability makes it desirable for just about any application, and it has a
decent set of built in types. Java is still not as high-level as Lisp or Prolog, and not
as fast as C, making it best when portability is paramount.
• Python- This language does not have widespread acceptance yet, but several
people have suggested to me that it might end up passing Java soon. According to
Peter Norvig, "Python can be seen as either a practical (better libraries) version of
Scheme, or as a cleaned-up (no $@&%) version of Perl."

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What is AI?

34. CATEGORIZATION OF
INTELLIGENT SYSTEMS
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Views of AI
(understand and build intelligent entities)
The area of CS that focuses on creating machines that can
engage on behaviors that human consider intelligent
Thinking humanly Thinking rationally
Acting humanly Acting rationally
Modern AI focuses on acting rationally

36. Systems that think like humans:
cognitive modeling
• Humans as observed from ‘inside’
• How do we know how humans think?
– Introspection vs. psychological
experiments
• Cognitive Science
• “The exciting new effort to make computers
think … machines with minds in the full and
literal sense” (Haugeland)
• “[The automation of] activities that we
associate with human thinking, activities such
as decision-making, problem solving, learning
…” (Bellman)

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Acting (doing) humanly:
“The Turing Test Approach”
• “The study of how to make computers do things at which, at the
moment, people are better.” (Rich and Knight)
• Alan Mathison Turing (1912-1954)
• A.M. Turing Award…..ACM's most prestigious technical award
is accompanied by a prize of $250,000. It is given to an
individual selected for contributions of a technical nature made
to the computing community. Financial support of the Turing
Award is provided by the Intel Corporation and Google Inc.
• Suggested major components of AI: Natural language
processing; Knowledge Representation; Automated reasoning;
Machine Learning
• Predicted that by 2000, a machine might have a 30% chance of
fooling a lay person for 5 minutes

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The imitation game: “Computing machinery and intelligence”
devised by Alan Turing in 1950 defines intelligent behavior as the
ability to achieve human level performance in all cognitive tasks,
sufficient to fool an interrogator.
Test passes if human interrogator cannot distinguish AI system
from human when interrogated via a teletype (a computer
keyboard and screen) 70% of the time
Original Turing Test abstracts out physical interaction
Total Turing Test adds Computer Vision (perception) and Robotics
(manipulation)

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Systems that act like humans
• natural language processing to enable it to
communicate successfully in English (or some
other human language)
• knowledge representation to store information
provided before or during the interrogation
• automated reasoning to use the stored
information to answer questions and to draw
new conclusions
• machine learning to adapt to new
circumstances and to detect and extrapolate
patterns.

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ELIZA
(one of the first chatterbots in existence)
ELIZA was a computer program and an early example of primitive
natural language processing.
ELIZA operated by processing users' responses to scripts, the
most famous of which was DOCTOR.
In this mode, ELIZA mostly rephrased the user's statements as
questions and posed those to the 'patient.'
ELIZA was written by Joseph Weizenbaum between 1964 to
1966.
In DOCTOR mode, ELIZA might respond to "My head hurts" with
"Why do you say your head hurts?"
The response to "My mother hates me" would be "Who else in
your family hates you?"
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.

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Reverse Turing Test
• STANDARD TURING TEST: judge is human
• REVERSE TURING TEST: judge is computer
The challenge would be for the computer to be able to determine
if it were interacting with a human or another computer.
• CAPTCHA is a form of reverse Turing test. Before being
allowed to perform some action on a website, the user is
presented with alphanumerical characters in a distorted graphic
image and asked to type them out. This is intended to prevent
automated systems from being used to abuse the site.
The rationale is that software sufficiently sophisticated to read and
reproduce the distorted image accurately does not exist (or is
not available to the average user), so any system able to do so
is likely to be a human.

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Thinking rationally (ideally):
“The Laws of Thought Approach"
• Humans are not always ‘rational’
• Logic can’t express everything (e.g. uncertainty)
• Aristotle was one of the first to attempt to codify “right
thinking”, i.e., irrefutable reasoning processes.
– Given correct premises; his syllogisms gave correct
conclusions
– eg. Socrates is a man; all men are mortal. → Socrates is
mortal.
• Formal logic provides a precise notation and rules for
representing and reasoning with all kinds of things in the
world.
• What is the purpose of thinking? What thought should I have
and what thought could I have?

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Laws of thought Approach emphasizes on correct inferences
Obstacles:
• it is not easy to take informal knowledge and state it in formal
terms, particularly when the knowledge is less than 100%
certain.
• there is a big difference between being able to solve a problem
"in principle" and doing so in practice. Even problems with just
a few dozen facts can exhaust the computational resources of
any computer unless it has some guidance as to which
reasoning steps to try first.

44. Systems that act rationally:
“Rational agent”
• Rational behavior: doing the right thing
• The right thing: that which is expected to
maximize goal achievement, given the available
information
• Giving answers to questions is ‘acting’.

45. Rational agents
 An agent is an entity that perceives and acts
 This course is about designing rational agents
 For any given class of environments and
tasks, we seek the agent (or class of agents)
with the best performance
 computational limitations make perfect
rationality unachievable
 → design best program for given machine
resources

46. From the above two definitions, we can see
that AI has two major roles:
– Study the intelligent part concerned with
humans.
– Represent those actions using computers.

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Acting rationally (ideally):
The Rational Agent Approach
Acting so as to achieve one’s goals, given one’s beliefs (assuming
them to be correct). Does not necessarily involve thinking.
• Advantages
– More general than the “laws of thought” approach.
– More amenable to scientific development than human
behavior or human thought based approaches.
• Problems
– Always doing the right thing is not possible in complicated
environments
– Computational demands are just too high
A basic agent is just something that perceives and acts

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• Cognitive skills required:
– Ability to represent knowledge and reason with it
– Ability to generate comprehensible sentences in natural
language
– Ability to perceive to get better idea of what an action might
achieve
• Rational agent: doing the right thing ( that which is expected to
achieve best expected outcome, given the available
information)
Doesn't necessarily involve thinking – e.g., blinking reflex –
but thinking should be in the service of rational action
Requires same skills as for Turing test and act even when
no provably correct way to act
Are broader in scope than previous ones.
Aristotle Every act and every enquiry, and similarly every
action and pursuit, is thought to aim at some good.

49. COMPONENTS OF AI
PROGRAM
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50. Prof Saroj Kaushik 50
Components of AI Program
• AI techniques must be independent of
the problem domain as far as possible.
• AI program should have
– knowledge base
– navigational capability
– inferencing

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Knowledge Base
• AI programs should be learning in
nature and update its knowledge
accordingly.
• Knowledge base consists of facts and
rules.
• Characteristics of Knowledge:
– It is voluminous in nature and requires
proper structuring
– It may be incomplete and imprecise
– It may keep on changing (dynamic)

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Navigational Capability
• Navigational capability contains
various control strategies
• Control Strategy
– determines the rule to be applied
– some heuristics (thump rule) may be
applied

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Inferencing
• Inferencing requires
– search through knowledge base
and
– derive new knowledge

54. FOUNDATION OF AI
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AI Foundation / Pre-History
o Philosophy(428 B.C.- present)
• Logic (no formal expression)
• Aristotle first to formulate a precise set of rules of rational
derivation
• methods of reasoning… A dog is an animal, all animals
have four legs → all dogs have four legs
• The emergence of intelligence in a physical brain
• Foundations of learning language and rationality
o Mathematics(c.800- present)
• Formal representation and proof
• Main areas: logic, computation and probability
• Logic: Mathematical Formulation
• Algorithms: First Euclid’s algorithm… calculate GCD
(analyze (un)decidability and (in)tractability)
• Probability theory: uncertainty in AI

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AI Foundation / Pre-History
o Economics(1776- present)
• Formal theory for rational decision making
• The concept of utility
• Decision theory (expected utility)
• Game theory (distributed models)
• Markov Models (OR)
o Neuroscience(1861- present)
• Broca study of aphasia ca → functional areas in brain
• Models for memory
• Basic model for action generation
o Psychology(1879- present)
• Behaviorism- study only objective measures of percepts
• Cognitive psychology- cognitive science- adaptation
• Reasoning- action generation and derivation

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AI Foundation / Pre-History
o Computer Engg.(1940- present)
• construction of efficient computers
• Languages for efficient implementation-
FORTRAN, LISP, PROLOG, BASIC, PASCAL,
C/C++, JAVA…
o Control theory and cybernetics(1948- present)
• Computer control of physical systems
• Basis for development of robotics, vision, language
processing
o Linguistics(1957- present)
• For understanding natural languages
• Formal languages
• Syntactic and semantic analysis
• Knowledge representation

58. SUBAREAS OF AI
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59. Subareas of AI
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Search
Vision
Planning
Machine
Learning
Knowledge
RepresentationLogic
Expert
SystemsRoboticsNLP

60. Prof Saroj Kaushik 60
Sub-areas of AI
– Knowledge representation
– Theorem proving
– Game playing
– Reasoning dealing with uncertainty and decision making
– Learning models, inference techniques, pattern recognition,
search and matching etc.
– Logic (fuzzy, temporal, modal) in AI
– Planning and scheduling
– Natural language understanding
– Computer vision
– Understanding spoken utterances
– Intelligent tutoring systems
– Robotics
– Machine translation systems

61. APPLICATIONS OF AI
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Applications of AI
• IBM supercomputer Deep Blue defeated the reigning world chess champion
Garry Kasparov (at grandmaster level) in 1997
• Proved a mathematical conjecture (Robbins conjecture) unsolved for
decades
• No hands across America (driving autonomously 98% of the time from
Pittsburgh to San Diego)
• ALVINN, grand challenge; cars can by now drive 200 km autonomously
• During the 1991 Gulf War, US forces deployed an AI logistics planning and
scheduling program that involved up to 50,000 vehicles, cargo, and people
• NASA's on-board autonomous planning program controlled the scheduling
of operations for a spacecraft
• Proverb solves crossword puzzles better than most humans
• MEDICAL EXPERT SYSTEM in 1980 is the first expert level performance
diagnosis of blood infections, diabetes, muscle diseases
• CHESS examines 5 billion positions per second
• ROBOTIC races in desert and urban environments by fully autonomous
vehicles; succeeded
• 2006: face recognition software available in consumer cameras

63. AI Applications
• Autonomous Planning & Scheduling:
– Telescope scheduling
– Analysis of data
– Autonomous rovers
• Medicine:
– Image analysis and enhancement
– Image guided surgery
• Robotic toys:
• Games:
• Transportation:
– Autonomous vehicle control

64. CURRENT TRENDS IN AI
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AI APPLICATIONS: consumer marketing

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AI APPLICATIONS: Identification Technologies

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AI APPLICATIONS: intrusion detection

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AI APPLICATIONS: predicting stock market

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AI APPLICATIONS: Machine Translation
• Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no
common language
– or: you are shipping your software manuals to 127 countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this
• How hard is automated translation
– very difficult! e.g., English to Russian
– “The spirit is willing but the flesh is weak” (English)
– “the vodka is good but the meat is rotten” (Russian)
– not only must the words be translated, but their meanings also!
– is this problem “AI-complete”?
• Nonetheless....
– commercial systems can do a lot of the work very well (e.g.,restricted
vocabularies in software documentation)
– algorithms which combine dictionaries, grammar models, etc.
– Recent progress using “black-box” machine learning techniques

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AI and Web Search

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Latest Perception of AI
• Three typical components of AI Systems
THE WORLD
Perception Action
Reasoning

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Recent AI
• Heavy use of
– probability theory
– decision theory
– statistics
– logic (fuzzy, modal, temporal)

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Intelligent Systems in Your Everyday Life
• Post Office
– automatic address recognition and sorting of mail
• Banks
– automatic check readers, signature verification systems
– automated loan application classification
• Customer Service
– automatic voice recognition
• The Web
– Identifying your age, gender, location, from your Web surfing
– Automated fraud detection
• Digital Cameras
– Automated face detection and focusing
• Computer Games
– Intelligent characters/agents

74. BEYOND SYLLABUS
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75. – more powerful and more useful computers
– new and improved interfaces
– solving new problems
– better handling of information
– relieves information overload
– conversion of information into knowledge
Some Advantages of Artificial
Intelligence

76. The Disadvantages
– increased costs
– difficulty with software development - slow
and expensive
– few experienced programmers
– few practical products have reached the
market as yet.

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Question Bank
• Explain information, knowledge and intelligence
• What is AI? Explain components of AI with suitable eg. Or block
diagram.
• What do you mean by Intelligent agent? Explain various types.
State limitation of each and how is it overcome in other.
• Explain structure of intelligent agents that keep track of the
world.
• Describe environment simulator programs with performance
measure that can be used as test beds for agent programs.
• Consider vacuum cleaner problem and explain
➢ How is it rational? Which behavior will be irrational.
➢ Give success function. Explain performance measure.