Introduction to AI - Abdullah Alsheddy

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ARTIFICIAL INTELLIGENCE:
INTRODUCTION



Short presentation


Dr. Abdullah Alsheddy

د
.
يدشلا زيزعلادبع اللهدبع

Email :
alsheddy@ccis.imamu.edu.sa

Office: FR
64

Textbook:


S. Russell and P. Norvig Artificial Intelligence: A Modern Approach, Prentice
Hall,
3
rd Edition,
2009

Grading:


Quizzes/Presentation/Participation (
20
%)


Project (
20
%)


Midterm test (
20
%)


Final Exam:
40
%


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Artificial Intelligence : Introduction

2

Course overview


Introduction and Agents (chapters 1,2)


Search (chapters 3,4,5,6)


Logic (chapters 7,8,9)


Planning (chapters 11,12)


Uncertainty (chapters 13,14)


Learning (chapters 18,20)


Robotics (chapter 25,26)

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Artificial Intelligence : Introduction

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Chapter
1
: Outline


What is AI


A brief history


The state of the art



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Artificial Intelligence : Introduction

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What is AI?


Intelligence:


“the capacity to learn and solve problems” (Websters dictionary)


in particular,



the ability to solve novel problems


the ability to act rationally


the ability to act like humans




Artificial Intelligence


build and understand intelligent entities or agents


2
main approaches: “engineering” versus “cognitive modeling”

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Artificial Intelligence : Introduction

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Intelligent

behavior

Humans

Computer

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Artificial Intelligence : Introduction

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Why AI?


Cognitive Science:

As a way to understand how natural minds
and mental phenomena work


e.g., visual perception, memory, learning, language, etc.


Philosophy:

As a way to explore some basic and interesting
(and important) philosophical questions


e.g., the mind body problem, what is consciousness, etc.


Engineering:

To get machines to do a wider variety of useful
things


e.g., understand spoken natural language, recognize individual people
in visual scenes, find the best travel plan for your vacation, etc.


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Artificial Intelligence : Introduction

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Weak vs. Strong AI


Weak AI
: Machines can be made to behave as if they were
intelligent



Strong AI
: Machines can have consciousness



Subject of fierce debate among philosophers and AI
researchers.



E.g. Red Herring
article

and
responses


http://groups.yahoo.com/group/webir/message/
1002

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Artificial Intelligence : Introduction

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Artificial Intelligence : Introduction

9


AI Characterizations




AI Characterizations





Discipline that systematizes and automates
intellectual tasks to create machines that :





Act like humans

System passing the Turing Test (1950)

Learning from Knowledge (adapt)

Representing Knowledge (memorize)

Solve
Pb

(argue)

Understanding (communicate)

Theoretical

Act rationally

Rational agent (199X)

acts according to his beliefs

to reach goals

(not only logical)

Pragmatic

Think like humans

Cognitive modeling



(GPS (Newel & Simon,61))


Complex

Think rationally


logical thinking

Pascal [1623
-
1662] (calculating machine)

Leibniz [1646
-
1716] (reasoning machine)

Babbage [1792
-
1871] (Analytical Engine)

limited

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Artificial Intelligence : Introduction

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Systems that
act

like humans


When does a system behave intelligently?


Turing (
1950
)
Computing Machinery and Intelligence


"Can machines think?"


"Can machines behave intelligently?"


Operational test of intelligence: imitation games








Test requires the collaboration of major components of AI: knowledge,
reasoning, language understanding, learning, …

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Artificial Intelligence : Introduction

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Interrogator interacts with a computer and

a person.



Computer passes the Turing test if

interrogator cannot determine which is

which.


Systems that
act

like humans


AI is the art of creating machines that perform
functions that require intelligence when
performed by humans


Methodology: Take an intellectual task at which
people are better and make a computer do it


Turing test

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Artificial Intelligence : Introduction

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Prove a theorem


Play chess


Plan a surgical operation


Diagnose a disease


Navigate in a building

Systems that
think

like humans


How do humans think?


Requires scientific theories of internal brain activities (cognitive model):


How to validate? requires :


Predicting and testing human behavior


Identification from neurological data


Brain imaging in action


Cognitive Science vs. Cognitive neuroscience vs. Neuroimaging


They are now distinct from AI


Share that the available theories do not explain
anything resembling human intelligence.


Three fields share a principal direction.

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Systems that
think

rationally


Capturing the laws of thought


Aristotle: What are ‘correct’ argument and thought
processes?


Correctness depends on irrefutability of reasoning processes.


This study initiated the field of logic.


The logicist tradition in AI hopes to create intelligent systems using logic
programming.


Problems:


Not all intelligence is expressed by logic behavior


What is the purpose of thinking? What thought should one have?

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Artificial Intelligence : Introduction

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Systems that
act

rationally


Rational behavior: “doing the right thing”


The “Right thing” is that what is expected to
maximize goal
achievement given the available information
.



Can include thinking, yet in service of
rational action.


Action without thinking: e.g. reflexes.

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Systems that
act

rationally


Two advantages over previous approaches:


More general than law of thoughts approach


More amenable to scientific development.



Yet rationality is only applicable in
ideal

environments.



Moreover rationality is not a very good model of
reality.


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Think/Act Rationally


Always make the best decision given what is
available (knowledge, time, resources)


Perfect knowledge, unlimited resources


logical
reasoning


Imperfect

knowledge, limited resources


(limited)
rationality

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Artificial Intelligence : Introduction

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Connection to economics, operational research,


and control theory


But ignores role of consciousness, emotions,


fear of dying on intelligence

Rational agents



An agent is an entity that perceives and acts


This course is about designing rational agents


An agent is a function from percept histories to actions:




For any given class of environments and task we seek the agent (or class
of agents) with the best performance.


Problem: computational limitations make perfect rationality
unachievable.

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
f
:
P
*

A
Foundations of AI


Different fields have contributed to AI in the form of
ideas, view points and techniques.


Philosophy
: Logic, reasoning, mind as a physical system, foundations of learning,
language and rationality.


Mathematics
: Formal representation and proof algorithms, computation,
(un)decidability, (in)tractability, probability.


Psychology
: adaptation, phenomena of perception and motor control.


Economics
: formal theory of rational decisions, game theory.


Linguistics
: knowledge representation, grammar.


Neuroscience
: physical substrate for mental activities.


Control theory
: homeostatic systems, stability, optimal agent design.

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A brief history


What happened after WWII?


1943
: Warren Mc Culloch and Walter Pitts: a model of artificial
boolean neurons to perform computations.


First steps toward connectionist computation and learning (Hebbian learning).


Marvin Minsky and Dann Edmonds (
1951
) constructed the first neural network
computer



1950
: Alan Turing’s “Computing Machinery and Intelligence”


First complete vision of AI.


Idea of Genetic Algorithms

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A brief history (
2
)


The birth of (the term) AI (
1956
)


Darmouth Workshop bringing together top minds on automata theory,
neural nets and the study of intelligence.


Allen Newell and Herbert Simon: The logic theorist (first non
-
numerical thinking program
used for theorem proving).


For the next
20
years the field was dominated by these participants.



Great expectations (
1952
-
1969
)


Newell and Simon introduced the General Problem Solver.


Imitation of human problem
-
solving


Arthur Samuel (
1952
-
) investigated game playing (checkers ) with great success.


John McCarthy(
1958
-
) :


Inventor of Lisp (second
-
oldest high
-
level language)


Logic oriented, Advice Taker (separation between knowledge and reasoning)

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A brief history (
3
)


The birth of AI (
1956
)


Great expectations continued ..


Marvin Minsky (
1958
-
)


Introduction of microworlds that appear to require intelligence to solve: e.g. blocks
-
world.


Anti
-
logic orientation, society of the mind.


Herbert Gelernter (
1959
) : constructed the geometry theorem Prover.


Artur Samual (
1952
-
1956
) : a series of programs for checkers.


Collapse in AI research (
1966
-

1973
)


Progress was slower than expected.


Unrealistic predictions.


Some systems lacked scalability.


Combinatorial explosion in search.


Fundamental limitations on techniques and representations.

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Artificial Intelligence : Introduction

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A brief history (
4
)


AI revival through knowledge
-
based systems
(
1969
-
1970
)


General
-
purpose vs. domain specific


E.g. the DENDRAL chemistry project (Buchanan et al.
1969
)


First successful knowledge intensive system.


Expert systems


MYCIN to diagnose blood infections (Feigenbaum et al.)


Introduction of uncertainty in reasoning.


Increase in knowledge representation research.


Logic, frames, semantic nets, …


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Artificial Intelligence : Introduction

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A brief history (
5
)


AI becomes an industry (
1980
-

present)


First successful commercial expert system R
1
at DEC (McDermott,
1982
)


Fifth generation project in Japan (
1981
) : a
10
-
year plan to build intelligent computer
running Prolog.


American response …: US formed the microelectronics and Computer Technology
Corporation designed to assure national competitiveness (chip design and human
-
interface
research)


Puts an end to the AI winter.


AI industry boomed from a few million to billion dollars in
1988
. Period called “AI winter” in
which many companies suffered as they failed to deliver on extravagant promises
.


Connectionist revival (
1986
-

present)


Parallel distributed processing (
RumelHart

and McClelland,
1986
);
back
-
propagation learning (computer science and psychology).

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Artificial Intelligence : Introduction

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A brief history (
6
)


AI becomes a science (
1987
-

present)


In speech recognition: hidden
markov

models


In neural networks


In uncertain reasoning and expert systems: Bayesian network formalism


Problem solving






The emergence of intelligent agents (
1995
-

present)


The whole agent problem:


How does an agent act/behave embedded in real environments with continuous sensory
inputs


“Ideally, an intelligent agent takes the best possible action in a situation : study the
problem of building intelligent agents in this sense”.

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State of the art : AI today
1
/
2


Autonomous planning and scheduling : on
-
board autonomous planning
program to control the scheduling of operations for a spacecraft (Jonhson
et al.,
2000
).


Game playing : IBM’s Deep Blue became the first computer program to
defeat the world champion in chess match (Goodman and Keene,
1997
),


Autonomous control : the ALVINN computer vision system was trained to
steer a car to keep it following a lane (for
2850
miles ALVINN was in
control of steering in
98
%, only
2
% for human control mostly at exit ramps).


Diagnosis : medical diagnosis programs based on probabilistic analysis
have been able to perform at level of an expert physician in several areas
in medicine (Heckerman
1991
).


Robot driving: DARPA grand challenge
2003
-
2007
.

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Stanley RobotStanford Racing Team

www.stanfordracing.org


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Artificial Intelligence : Introduction

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Major research areas
(Applications)


Natural Language Understanding


Image, Speech and pattern recognition


Uncertainty Modeling


Problem solving


Knowledge representation


…..

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AI Success Story :
Medical expert systems


Antibiotics & Infectious

Diseases


Cancer


Chest pain


Dentistry


Dermatology


Drugs &

Toxicology


Emergency


Epilepsy


Family Practice


Genetics



Geriatrics

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Programs listed by Special Field



Gynecology


Imaging Analysis


Internal Medicine


Intensive Care


Laboratory Systems


Orthopedics


Pediatrics


Pulmonology & Ventilation


Surgery & Post
-
Operative
Care


Trauma Management

Pattern Recognition Applications

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Handwriting and document recognition



Signature, biometrics (finger, face, iris, etc.)




T
r
afic monitoring, Remote Sensing




guided missile, target homing

Future of AI

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Making AI Easy to use


Easy
-
to
-
use Expert system building tools


Web auto translation system


Recognition
-
based Interface Packages


Integrated Paradigm


Symbolic Processing + Neural Processing


AI in everywhere, AI in nowhere



AI embedded in all products


Ubiquitous Computing, Pervasive Computing

Quiz


Does a plane fly?


Does a boat swim?


Does a computer think?

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