Chapter 1: An introduction to Artificial Intelligence
What exactly is Artificial Intelligence? Artificial Intelligence is the study of
how to make computer do things which, at the moment, people do better.
This definition is of course somewhat ephemeral because of its reference to
the current state of computer science. And it fails to include some areas of
potentially very large impact, namely problems that cannot now be solved
well by either computer or people. But it provides a good outline of what
constitutes artificial Intelligence, and it avoids the philosophical issues that
dominate attempts to define the meaning of either artificial or intelligence.
Artificial Intelligence is the area of engineering focusing on creating
machines that can engage on behaviours that human considers intelligence.
The ability to create intelligent machines has intrigued humans since ancient
time and today with the advent of computer and 50 years of research into AI
programing techniques, the dream of smart machines is becoming reality.
Researchers are creating systems which can mimic human thoughts,
understand speech, beat the best human chess
player, and countless other feats never before
possible. The military is applying AI logic to its hi-
tech systems, and in the near future Artificial
Intelligence may impact our lives.
John McCarthy, who coined the term „Artificial
Intelligence‟ in 1956, at Massachusetts Institute of
Technology, defines it as "the science and
John McCarthy engineering of making intelligent machines”
Artificial intelligence can be said a crossbreeding of a lot of fields:
Philosophy Logic, methods of reasoning, mind as physical system,
foundations of learning, language, rationality.
Mathematics Formal representation and proof, algorithms,
computation, (un)decidability, (in)tractability
Statistics Modeling uncertainty, learning from data
Economics Utility, decision theory, rational economic agents
Neuroscience Neurons as information processing unit
Psychology How do people behave, perceive, process cognitive
information, represent knowledge
Computer Building fast computers
Control Theory Design systems that maximize an objective function over
Linguistics Knowledge representation, grammars
Artificial Intelligence is a broad topic, consisting of different field from
machine vision to expert system.
In order to classify machines as “thinking”, it is necessary to define
intelligence. To what degree intelligence consists of, for example, solving
complex problems, or making generalization and relationship? And what
about perception and comprehension? Researches into the areas of learning
of language, and of sensory perception have aided scientist in building
intelligent machines. One of the most challenging approaches experts facing
is building systems that mimic the behavior of human brain, made up of
billions of neurons, and arguably the most complex matter in the universe.
Perhaps the best way to gauze the intelligence is British computer scientist
Alan Turing‟s test. He stated that a computer would deserve to be called
intelligent if it could deceive a human into believing that it was a human.
A computer would need the followings to pass
Natural language processing: to
communicate with examiner.
Knowledge representation: to store and
retrieve information provided before or
Automated reasoning: to use the stored
information to answer questions and to draw
Machine learning: to adapt to new circumstances and to detect and
Vision (for Total Turing test): to recognize the examiner‟s actions and
various objects presented by the examiner.
Motor control (total test): to act upon objects as requested.
Other senses (total test): such as audition, smell, touch, etc.
Artificial intelligence has come a long way from
its early roots, driven by dedicated researchers.
The beginning of AI reaches back before
electronics, to philosophers and mathematicians
such as Boole and other theorizing on principles
that were used as the foundation of AI logic. AI
really began to intrigue researchers with the
invention of computer in 1943. The technology
was finally available, or so it seemed, to simulate
George Boole intelligent behavior. Over the next five decades,
despite many stumbling blocks, AI has grown from a dozen researchers to
thousands of engineers and specialists; and from programs capable of playing
checkers, to system designed to diagnose disease.
Chapter 2: The history of Artificial Intelligence
Evidence of Artificial Intelligence folklore can be traced back to ancient
Egypt, but with the development of electronic computer in 1941, the
technology finally become available to create machine intelligence. The term
Artificial intelligence was first coined in 1956, at Dartmouth conference, and
since then Artificial Intelligence has expanded because of the theories and
principles developed by its dedicated researchers. From its birth 4 decades
ago, there have been a variety of AI programs, and they have impacted other
Here the brief history of AI is given:
1943 McCulloch & Pitts: Boolean circuit model of brain
1950 Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted
1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry
1965 Robinson's complete algorithm for logical reasoning
1966-73 AI discovers computational complexity, neural network
research almost disappears
1969-79 Early development of knowledge-based systems
1980 AI becomes an industry
1986 Neural networks return to popularity
1987 AI becomes a science
1995 The emergence of intelligent agents
Pre-history of AI:
Through history, people thought of mythic “artificial” robots
golden robots of Hephaestus and Pygmalion's Galatea
alchemical means of placing mind into matter
More specific, tangible advances
5th century B.C.
Aristotle invented syllogistic logic, the first formal
deductive reasoning system.
Talking heads were said to have been created (Roger
Bacon and Albert the Great).
Ramon Lull, Spanish theologian, invented machines for
discovering nonmathematical truths through combinatory.
Invention of printing using moveable type. Gutenberg
Bible printed (1456).
Clocks, the first modern measuring machines, were first
produced using lathes.
Clockmakers extended their craft to creating mechanical
animals and other novelties.
17th century - The revolution of thinking about thinking
Descartes proposed that bodies of animals are nothing
more than complex machines (strong AI).
Variations and elaborations of Cartesian mechanism.
Hobbes published The Leviathan, containing a material
and combinatorial theory of thinking.
Pascal created the first mechanical digital calculating
Leibniz improved Pascal's machine to do multiplication
& division (1673) and envisioned a universal calculus of
reasoning by which arguments could be decided
18th century – Mechanical toys
Vaucanson‟s Duck Von Kempelen‟s phony
mechanical chess player
19th century – Frankenstein‟s birth
George Boole developed a binary algebra representing
(some) "laws of thought," published in The Laws of
Charles Babbage and Ada Byron (Lady Lovelace)
worked on programmable mechanical calculating
Mary Shelley published the story of Frankenstein's
Crossing the century bridge
Behaviorism was expounded by psychologist
Edward Lee Thorndike in "Animal Intelligence."
Pre-birth of AI:
Beginning of the 20th century
Russell and Whitehead published Principia Mathematica.
Capek's play “Rossum's Universal Robots” produced in 1921
(London opening, 1923). First use of the word 'robot' in English.
McCulloch and Pitts publish "A Logical Calculus of the Ideas
Immanent in Nervous Activity" (1943), laying foundations for
Rosenblueth, Wiener and Bigelow coin the term cybernetics
Bush published As We May Think (1945) a prescient vision of
the future in which computers assist humans in many activities.
The era of computer:
In 1941 an invention revolutionized every aspect of the storage and
processing of information. That invention developed both in U.S. and
Germany was the electronic computer.
The 1949 innovation, the stored program computer, made the job of entering
a program easier, and the advancements in computer theory lead to computer
science, and eventually artificial intelligence. With the invention of an
electronic means of processing data, came a medium
that made AI possible.
The beginning of AI:
Although the computer provided the technology
necessary for AI, it was not until early 1950‟s that the
link between human intelligence and machines was Norbert Wiener
really observed. Norbert Wiener was one of the first
Americans to make observation on the principle of feedback theory. What so
important about his research into feedback loops was that Wiener theorized
that all intelligent behavior was the result of feedback mechanism.
In late 1955, Newell and Simon developed „The logic Theorist‟, considered
by many to the first AI program. The program representing each problem as a
tree model, would attempt to solve it by selecting the branch that would most
likely result in the correct conclusion. The impact that „the logic theorist‟
made on both the public and the field of AI has made it a crucial stepping
stone in developing the AI field.
In 1956, John McCarthy, regarded as the father of AI organized a conference
to draw the talent and expertise of others interested in machine intelligence
for a month of brain storming. He invited them to Vermont for “The
Dartmouth Summer Research Project on Artificial Intelligence”. Although
not a huge success, the Dartmouth conference brought together the founders
in AI, and served to lay the groundwork for the future if AI research.
Research was placed upon creating system that could efficiently solve
problem, by limiting the search, such as the logic theorist and second, making
systems that could learn themselves.
In 1957, the first version of a new program-„The General Problem Solver‟
was tested. The program developed by same pair which developed „The
Logic Theorist‟. The GPS was an extension of Wiener‟s feedback principle,
and was capable of solving a greater extent of common sense problems.
While more program ware being produced, McCarthy was busy developing a
major breakthrough in AI history. In 1958 McCarthy announced his new
development; the LISP language, which is still used today. LISP stands for
„list processing‟, and was soon adopted as the language of choice among
most AI developers.
Chapter 3: Branches of AI
An AI machine should have and show some intelligent behaviours:
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
Depending upon these behaviors AI can be classified as following:
A system that approximates the way a human sees,
hears, and feels objects
Capture, store, and manipulate visual images and
Mechanical and computer devices that perform
tedious tasks with high precision
Stores knowledge and makes inferences
Computer changes how it functions or reacts to
situations based on feedback
Natural language processing
Computers understand and react to statements and
commands made in a “natural” language, such as
Computer system that can act like or simulate the
functioning of the human brain
Depending upon the above classification it is realized that AI can have
several branches regarding different job. The following is detailed
classification with brief description.
What a program knows about the world in general the facts of specific
situation in which it must act, and its goals are all represented by
sentences of some mathematical, logical language. The program
decides what to do by inferring that certain actions are appropriate
for achieving its goals.
AI programs often examine large number of possibilities, e.g. moves in
chess game of inferences by a theorem proving program. Discoveries
are continuously made about how to do this more efficiently in various
When a program makes observation of some kind, it is often
programmed to compare what it sees with a pattern. For example, a
vision program may try to match a pattern of eyes and a nose in a
scene in order to find a face. More complex pattern e.g. in a natural
language test, in a chess position, or in the history of some event are
also studied. These more complex patterns require quite different
methods than do the simple patterns that have been studied the
Facts about the world have to be represented in some way. Usually
languages of mathematical logic are used.
Mathematical, logical deduction is adequate for some purposes, but
new methods of non-monotonic inference have been added to logic
since 1970s. The simplest kind of non-monotonic reasoning is default
reasoning in which a conclusion is to be inferred by default, but the
conclusion can be withdrawn if there is evidence to the contrary. For
example when we hear of a bird, we may infer that it can fly. But this
conclusion can be reversed when we hear that it is a Penguin. It is the
possibility that a conclusion may have to be withdrawn that constitutes
the non-monotonic character of the reasoning. Ordinary logical
reasoning is monotonic in that the set of conclusions that can be drawn
from a set of premises is a monotonic increasing function of the
premises. Circumscription is another form of non-monotonic
Common sense knowledge and reasoning
This is the area in which AI is farthest from human level, in spite of the
fact that it has been an active research area since the 1950s. while there
has been considerable progress, e.g. in developing systems of non-
monotonic reasoning and other theories of action, yet more new ideas
Learning from experience
The approaches to AI based on connectionism and neural nets
specialize in that. There is also learning of laws expressed in logic.
Programs can only learn what facts or behaviors their formalism can
represent, and unfortunately learning systems are almost all based on
very limited abilities to represent information.
Planning programs start with general facts about the world, especially
facts about the effects of actions, facts about the particular situation
and a statement of a goal. From these they generate a strategy for
achieving the goal. In the most common cases, the strategy is just a
sequence of actions.
This is a study of the kinds of knowledge that are required for solving
problem in the world.
This is the study of kinds of things that exist. In AI, the program and
sentences deal with various kinds of objects, and we study what these
kinds are and what their basic properties are. Emphasis on Ontology
begins in the 1990s.
A heuristic is a way of trying to discover something or an idea
imbedded in a program. Heuristic predicates that compare two nodes in
a search tree to see if one is better than the other, i.e. constitutes an
advance towards the goal, may be more useful.
Chapter 4: Approaches: methods used to create intelligence
In the quest to create intelligent machines, the field of artificial intelligence
has been split into several different approaches based on the opinions about
the most promising methods and theories. These rivaling theories have lead
researchers in one of two basic approaches: bottom-up and top-down.
Bottom-up theorist believe the best way to achieve artificial intelligence is to
build electronic replicas of the human brain‟s complex network of neurons,
while the top down approach attempts to mimic the brain‟s behavior with
Neural network and parallel computation
The human brain is made up of a web of billions of cells called neurons, and
understanding its complexity is seen as one of the last frontiers in scientific
research. It is the aim of AI researchers who prefer this bottom-up approach
to construct electronic circuits that act as neurons do in the human brain.
Although much of the working of the brain remains unknown, the complex
network of neurons is what gives human intelligent characteristics. By itself,
a neuron is not intelligent, but when grouped together, neuron are able to pass
electrical signal through networks.
Warren McCulloch, after completing medical school at Yale, along with
Walter Pitts, a mathematician
proposed a hypothesis to explain
the fundamentals of how neural
network made the brain work.
Based on experiments with
neurons, McCulloch and Pitts
Warren McCulloch showed that neurons might be Walter Pitts
considered devices for processing binary numbers. An important back of
mathematic logic, binary numbers, represented as 1‟s & 0‟s or true & false,
were also the basis of electronic computer. This link is the basis of compute
simulated neural network, also known as Parallel computing.
A century earlier the true/false nature of binary numbers is theorized, in 1854
by George Boole in his postulates concerning the laws of thought. Boole‟s
principle made up what is known as Boolean algebra, the collection of logic
concerning AND, OR, NOT operands. For example according to the laws of
thought the statement :( for example all apples are red)
Apples are red --------------- True
Apples are red AND oranges are purple --------------- False
Apples are red OR oranges are purple --------------- True
Apples are red AND oranges are NOT purple -------- True
Boole also assumed that the human mind works
according to these laws, it performs logical operation
that could be reasoned. Ninety years latter Claude
Shannon applied Boole’s principles in circuits, the
blueprint for electronic computers.
Top-down approaches: Expert systems
Because of the large storage capacity of computers, Claude Shannon
expert systems had the potential to interpret statistics,
in order to formulate the rules. An expert system works much like a detective
solves a mystery. Using the information, and logic or rules, an expert system
can solve the problem. For example if the expert system was designed to
distinguish birds it may have the following:
NO YES NO YES
Penguin YES Multi- NO Ostrich YES Crest?
Parrot Bald? NO Vulture
YES Large Eagle
NO Top-down approach logic
Charts like the previous one, represents the logic of expert systems. Using a
similar set of rules, experts can have a variety of applications. With improved
interfacing, computers may begin to find a larger place in society.
AI based game playing programs combine intelligence with entertainment.
On game with strong AI ties is chess. The greatest advances have occurred in
the field of games playing. The best computer chess programs are now
capable of beating humans. In May, 1997, an IBM super-computer called
Deep Blue defeated world chess champion Gary Kasparov in a chess match.
These chess playing program can see ahead twenty plus moves in advance
each move they make. In addition, the programs have an ability to get
progress ably better over time because of the ability to learn from experience.
Chess program do not play chess as human plays. In three minutes Deep
Thought (a master program) considers 126 million, while human chess
master on average consider less than two moves. Herbart Simon suggested
that, human chess masters are familiar with favorable board positions, and the
relationship with thousands of pieces in small areas. Computers on the other
hand, do not take hunches into account. The next move comes from exclusive
searches from all moves, and the consequences of the moves based in prion
learning. Chess program running on Cray supercomputer have attained a
rating of 2600(senior master), in the range of Gary Kasparov, the Russian
TOPIO. A robot, can play table
Deep Blue tennis
Chapter 5: Application of AI
What can we do with AI
We have been studying this issue of AI application for quite some time now
and know all the terms and facts. But what we all really need to know is what
we can do to get our hands on some AI today. How can we as individuals use
our own technology? We hope to discuss in depth but as firefly as possible,
so that you the consumer can use AI as it is intended.
First we should be prepared for a change. Our conservative ways stand in the
way of progress.AI is a new step that is very helpful to the society. Machines
can do jobs that require detailed instruction followed and mental alertness. AI
with its learning capabilities can accomplish those tasks but only if the
world‟s conservatives are ready to change and allow this be possibilities. It
makes us think about us how early man finally accepted the wheel as a good
invention, not something taking away from its heritage or tradition.
Secondly, we must be prepared to learn about the capabilities of AI. The
more use we get out of the machines the less work is required by us, in turn
less injury and stress to human beings. Human beings are a species that learn
by trying and we must be prepared to give AI a chance seeing AI as a
blessing, not an inhibition.
Finally we need to be prepared for the worst of AI. There is always the fear
that if AI is learning based; will machines learn that being rich and successful
is a good thing, then wage war against economic powers and famous people?
There is so many things that can go wrong with a new system, so we must be
as prepared as we can be for this new technology.
However even though the fear of machines is there, their capabilities are
infinite. Whatever we teach AI, they will suggest in the future if a possible
outcome arrives from it. AI are like children that need to be taught to be kind,
well mannered, and intelligent. If they are to make important decisions, they
should be wise. We as citizen need to make sure AI programmers are keeping
things on the level. We should be sure they are doing the job correctly, so
that no future accident occurs.
AIAI teaching computers computer
Does this sound a little redundant? Or may be a little redundant? Well just sit
back and let me explain. The Artificial Intelligence Application Institute has
many projects that they are working on to make their computer learn how to
operate themselves with less human input. To have more functionality with
less input is an operation for AI technology. I will discuss just two of these
projects: „AUSDA‟ and „EGRESS‟.
AUSDA is a program which examines software to see if it is capable of
handling the task you need performed. If it is not able or is not reliable,
AUSDA will instruct you on finding alternative software which would better
suit your needs. According to AIAI the software would provide solutions to
problems like “identifying the root causes of incidents in which the use of
computer software is involved, studying different software development
approaches and identifying aspects of these which are relevant to those root
causes producing guidelines for using and improving the development
approaches studied, and proving support in the integration of these
approaches, so that they can be better used for the development and
maintenance of safety critical software”.
Sure for the computer buffs this program is definitely good news. But what
about average person who thinks the mouse is just the computer foot paddle?
Where do they fit into computer technology? Well don‟t worry guys, because
we nerds are looking out for you too! Just ask AIAI what they have for you,
and it turns up the EGRESS is right down your alley. This is a program
which is studying human reactions to accidents. It is trying to make a model
of how people‟s reaction in panic moment saves lives. Although it seems like
in tough situation human would fall apart and have no idea what to do, it is in
fact the opposite. Quick decisions are usually made and are effective but not
flawless. These computer models will help rescuers make smart decision in
time of need. AI can‟t be positive all the time but suggest actions which we
can act out and therefore lead to safe rescues.
So AIAI is teaching computers do better computers and better people. AI
technology will never replace man but can be an extension of our body which
allows us to make more rational decision faster. And with institute like AIAI-
we continue each step to step forward into progress.
No worms in these apples
Apple Computer may not have ever been considered as the state of art in
Artificial Intelligence, but a second look should be given. Not only are todays
PC‟s becoming more powerful but AI influence is showing up in them. From
Macros to Voice Recognition technology, PC‟s are becoming our talking
buddies. Who else would go surfing with you on short notice-even if it is the
net? Who else would care to tell you that you have a business appointment
scheduled at 8:35 & 28 seconds and would notify you about it every minute
till you told it to shut up.
All power Macintoshes come with voice recognition. That‟s right – you tell
the computer to do what you want without it having to learn your voice. This
implication of AI in personal computer is still very crude but it does work
given the correct conditions to work in and a clear voice. Not to mention the
requirement of at least 16MBs of RAM for quick use. Also Apple‟s Newton
and other hand held notepad have script recognition. Cursive or paint can be
recognized by these notepad sized devices. With the Pen that accompanies
your silicon notepad you can write a little note to yourself which magically
changes into computer text if desired. No more complaining about sloppy
written reports if your compute can read your handwriting. If it can‟t read it
through- perhaps in the future, you can correct it by dictating your letters
Macros provide a huge stress relief as your computer does what you could do
more tediously. Macros are old but they are to an extent, intelligent. You
have taught the computer to something by doing it only once. In business
many times applications are upgraded. But the files must be converted. All of
the business records need to be changed into the new software‟s type. Macros
save the work of conversion of hundreds of files by a human by teaching the
computer to mimic the action of the programmer. Thus teaching the computer
a task that it can repeat whenever ordered to do so.
The scope of Expert System
As stated in the „approaches‟ section, an expert system is able to do the work
of a professional. Moreover a computer can be trained quickly, has virtually
no operating cost, never forgets what it learns, never calls in sick, retires or
goes on vacation. Beyond those, intelligent computers can consider a large
amount of information that may not be considered by humans.
But to what extent should these systems replace human experts? Or, should
they at all? For example some people once considered an intelligent computer
as a possible substitute for human control over nuclear weapons, citing that a
computer could respond more quickly to a threat. And many AI developers
were afraid of the possibilities of program like ELIZA, the psychiatrist and
the bond that humans were making with the computer. We can‟t however
overlook the benefits of having a computer expert. Forecasting the weather,
for example, relies on many variables, and a computer expert can more
accurately pool all of its knowledge. Still a computer can‟t rely on the
hunches of a human expert, which are sometimes necessary in predicting an
outcome.Expert systems have the power and range to aid the benefit, and in
some case replace humans, and computer experts, if used with discretion, will
benefit human kind.
Chapter 6: AI programs
ELIZA: An earlier AI program that simulated the behavior of a Rogerian
therapist. ELIZA’s knowledge about English and Psychology was coded in a
set of simple rule based on Complex and Approximate Matching, Conflict
Reasoning. Joseph Weizenbaum’s result of trying to make a program
converse in English amazed people when it appeared in 1960’s. the program
was able to converse about any subject, because of its stored subject
information in data banks. Another feature of ELIZA was its ability to pick up
speech patterns. 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.
A typical conversation is the following:
Girl: Men are all alike.
Eliza: IN WHAT WAY?
Girl: They're always bugging us about something or other.
Eliza: CAN YOU THINK OF A SPECIFIC EXAMPLE?
Girl: Well, my boyfriend made me come here.
Eliza: YOUR BOYFRIEND MADE YOU COME HERE
Girl: He says I'm depressed much of the time.
Eliza: I AM SORRY TO HEAR YOU ARE DEPRESSED
Girl: It's true. I am unhappy
Eliza: DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE
Girl: I need some help, that much seems certain.
Eliza: WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP
Girl: Perhaps I could learn to get along with my mother.
Eliza: TELL ME MORE ABOUT YOUR FAMILY
Girl: My mother takes care of me.
Eliza: WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU
Girl: My father.
Eliza: YOUR FATHER
Girl: You are like my father in some ways.
Eliza: WHAT RESEMBLANCE DO YOU SEE
Girl: You are not very aggressive but I think you don't want me to notice that.
Eliza: WHAT MAKES YOU THINK I AM NOT VERY AGGRESSIVE
Girl: You don't argue with me.
Eliza: WHY DO YOU THINK I DON'T ARGUE WITH YOU
Girl: You are afraid of me.
Eliza: DOES IT PLEASE YOU TO BELIEVE I AM AFRAID OF YOU
Girl: My father is afraid of everybody.
Eliza: WHAT ELSE COMES TO YOUR MIND WHEN YOU THINK OF
Eliza: DOES THAT HAVE ANYTHING TO DO WITH THE FACT THAT
YOUR BOYFRIEND MADE YOU COME HERE
As impressive as ELIZA‟s look, Wiezenbaum noted that it understood no
more of the girl‟s problem as an alarm clock understands when to go off. For
example, the last sentence gives the impression of comprehension, but in fact
the program is written so that at one point, it modifies the first sentence and
applies it as a response.
The General Problem Solver(GPS): General Problem Solver (GPS) was
a computer program created in 1957 by Herbert Simon, J.C. Shaw, and Allen
Newell intended to work as a universal problem solver machine. Any
formalized symbolic problem can be solved, in principle, by GPS. For
instance: theorems proof, geometric problems and chess playing. It was based
on Simon and Newell's theoretical work on logic machines.
While GPS solved simple problems such as the Towers of Hanoi that could
be sufficiently formalized, it could not solve any real-world problems
because search was easily lost in the combinatorial explosion of intermediate
states. The user defined objects and operations that could be done on the
objects and GPS generated heuristics by Means-ends analysis in order to
SAM: Sam is a program from Yale's AI lab which is able to read between
the lines, and assume certain facts.
John went to New York by bus. On the bus he talked to an old lady. When he
left the bus, he thanked the driver. He took the subway to Leone's. On the
subway his pocket was picked. He got off the train and entered Leone's. He
had some lasagna. When the check came, he discovered he couldn't pay. The
management told him he would have to wash dishes. When he left, he caught
a bus to New Haven.
Sam's comprehension: (Sam's answers are in italic.)
Why did John go to New York?
Because John wanted to go to Leone's
How did John get to Leone's?
John took a bus to New York and then he took the subway to Leone's.
Did anything unusual happen on the bus?
I don't remember anything
Did anything unusual happen on the subway?
A thief picked John's pocket.
What did John order?
John ordered lasagna.
Did anything unusual happen at the restaurant?
John discovered that he could not pay the check and so he had to wash
Why couldn't John pay the check?
Because John didn't have any money.
Why didn't John have any money?
Because a thief picked John's pocket.
How did John get back to New Haven?
John went from Leone's by subway and then he went to New Haven by bus.
How was John able to take the bus to New Haven?
Probably he had a ticket.
Sam's logic is represented in frames. For this story Sam might use a
restaurant frame, and a subway and bus frame. These frames spell out what
events in order most likely would occur in situations. Sam is impressive,
because it can draw conclusions from the story that are not specifically stated.
Also, with the last response, Sam shows that he can make conjectures without
information from the story, with the use of frames.
SHRDLU: It was an early natural language understanding computer
program, developed by Terry Winograd at MIT from 1968-1970. It was
written in the Micro Planner and Lisp programming language on the DEC
PDP-6 computer and a DEC graphics terminal. Later additions were made at
the computer graphics labs at the University of Utah, adding a full 3D
rendering of SHRDLU's "world".
The name SHRDLU was derived from ETAOIN SHRDLU, the arrangement
of the alpha keys on a Linotype machine, arranged in descending order of
usage frequency in English.
SHRDLU and block worlds
1. Pick up a red block
2. find a block which is taller than the one you are holding and put it into the
By "it," i assume that you mean
the block which is taller than the
one i am holding. OK.
3. what does the box contain?
The blue pyramid and the blue
4. what is the pyramid supported
5. how many blocks are not in
four of them
6. is at least one of them
narrowed than the one which I told you to pick up?
yes, the red cube
7. is it supported?
yes, by the table
8. can the table hold blocks?
9. the blue pyramid is mine
10. I own all blocks which are not red, but I don‟t own anything that supports
11. do I own the box?
12. do I own anything in the box?
Yes, two things: the blue box and the blue pyramid
13. will you please stack up both of the red blocks and either a green cube or
14. is there a large block behind a pyramid?
yes, three of them: a large red one, a large green cube and the blue one.
15. put a small one into the green cube which supports a pyramid
16. put the littlest pyramid on top of it
17. had you touched any pyramid before you put the green one on the little
Yes, the green one
18. when did you pick it up?
while i was stacking up the red cube, a large red block and a large green
to get rid of it
20. why did you do that?
to clear off the red cube
21. why did you clear off that cube?
to put it on a large green cube
22. why did you do that?
to stack the red cube, a large block, and a large green cube
23. why did you do that?
because you asked me to
With SHRDLU's comprehension and logic, AI reached a new level. The
name SHRDLU was actually borrowed from MAD magazine, and is made up
of, as its author Terry Wingrad explained, the seventh through twelfth
frequently used letters in the English language.
CYRUS: It is a MOP(memory organization packets) based program which
contains episodes taken from the life of a particular individual. It can answer
questions that require significant amounts of memory reconstruction.
IPP: This program accepts stories about terrorist attack and stores them in an
MOPTRANS: This program uses a MOP based memory to understand
sentences in one language and translates into another.
PROSPECTOR: This is a program that provides advices on mineral
DESIGN ADVISOR: It is a system that critiques chip design. It gives
advice to the chip designer who decides to accept or reject the advice.
NEUROGAMMON: This program is based on neural network that learns
from experience. This is one of the few game playing programs which relies
heavily on automatic learning.
State of the Artificial Intelligence
Deep Blue defeated the reigning world chess champion Garry
Kasparov 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)
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
“ARTIFICIAL INTRLLIGENCE” by Elaine Rich
& Kevin Knight