EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt

EL-SAYED A. El-Dahshan
SELDAHSHAN@EELU.EDU.EG
OFFICE: EELU- AIN SHAMS CENTRE.
PHONE: 33318447
ITF308-Artificial Intelligence
2022- 2023
Lecture 01

ITF308-Artificial Intelligence
• Course overview:
foundations of symbolic intelligent systems, Agents, search,
problem solving, learning, Knowledge representation, and
thinking & reasoning. Topics may be added or deleted.
• Prerequisites:
programming principles, discrete mathematics for computing,
software design and software engineering concepts. Good
knowledge of C++ and/or Java required for programming
assignments.

ITF308- Resources
Required textbook:
S. Russell and P. Norvig. Artificial Intelligence: A
Modern Approach. third edition, Pearson, 2010.

Grading
 Assignments (10%)
 Attendance (10%)
 Quizzes (10%)
 Midterm (20%)
 Final (50%)

AI: Goals
• Ambitious goals:
– understand “intelligent” behavior
– build “intelligent” agents / artifacts (artificial)
autonomous systems

What is Intelligence?
• Intelligence:
– capacity to learn and solve problems
– the ability to act rationally
•Hmm… Not so easy to define.
what is intelligence?
•Intelligence is the computational part of the ability to achieve
goals in the world.
Varying kinds and degrees of intelligence occur in people,
many animals and some machines.

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What is Artificial Intelligence?
Artificial intelligence (AI) is technology and a branch
of computer science that studies and develops intelligent
machines and software.
Major AI researchers and textbooks define the field as "the study
and design of intelligent agents", where an intelligent agent is a
system that perceives its environment and takes actions that
maximize its chances of success.
John McCarthy, who coined the term in 1955, defines it as "the
science and engineering of making intelligent machines".
 This field has close ties to psychology, philosophy,
engineering and cognitive science.

Different Types of Artificial Intelligence
• Modeling exactly how humans actually think
– cognitive models of human reasoning
• Modeling exactly how humans actually act
– models of human behavior (what they do, not how they think)
• Modeling how ideal agents “should think”
– models of “rational” thought (formal logic)
– note: humans are often not rational!
• Modeling how ideal agents “should act”
– rational actions but not necessarily formal rational reasoning
– i.e., more of a black-box/engineering approach
• Modern AI focuses on the last definition
– we will also focus on this “engineering” approach
– success is judged by how well the agent performs
-- modern methods are also inspired by cognitive & neuroscience (how
people think).

Different AI Perspectives
•Human Thinking
•Human Acting
•Rational Thinking
•Rational Acting
1. Systems that act like humans
2. Systems that think like humans 3. Systems that think rationally (optimally)
4. Systems that act rationally

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Artificial Intelligence Characterization
Artificial Intelligence is a branch of computer science that aims to produce
"intelligent" thought and/or behavior with machines. This field has close ties to
psychology, philosophy, and cognitive science .
Must a system think and act like humans to be intelligent?
Definitions of the goals of Artificial Intelligence systems can be divided into
four categories.
1) Systems that think like humans 2) Systems that think rationally
3) Systems that act like humans 4) Systems that act rationally
Category 1) "The exciting new effort to make computers think ... machines
with minds ,in the full and literal sense" (Haugland, 1985).
Category 2) "The study of mental faculties through the use of computational
models" (Charniak and McDermott, 1985). "The study of the computations that
make it possible to perceive, reason, and act" (Winston, 1992(.

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Category 3) "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 which, at the moment, people
are better" (Rich and Knight, 1991(
Category 4) "A field of study that seeks to explain and emulate
intelligent behavior in terms of computational processes" (Schalkoff, 1990)
"The branch of computer science that is concerned with the automation of
intelligent behavior" (Luger and Stubblefield, 1993(
Just as the term "intelligence" has multiple interpretations, so does the field
of Artificial Intelligence. Researchers are working toward their own
definition of these goals, with a variety of approaches .

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How do we know if we have achieved AI?
Since AI is a difficult area to define, what can we use to measure our success?
A Brittish mathematician named Alan Turing describing a way to
test intelligence. This has since been named "the Turing test" in
his honor.
The test proceeds as follows. A human interrogator is "talking" to
another entity (either a human or a computer) via a teletype (a
text only terminal). The interrogator can ask any questions they
would like for a certain amount of time and then they must
decide if they were talking with a human or a machine. If they
conclude that a computer was a human, then the computer must
be acting intelligently .

Acting humanly: Turing Test
• "Can machines think?“ "Can machines
behave intelligently?"
– Operational test for intelligent behavior: the
Imitation Game
AI system passes
if interrogator
cannot tell which one
is the machine.
Alan Turing
(interaction via written questions)
Turing (1950) "Computing machinery and intelligence”
No computer vision or robotics or physical presence required!

Turing Test:
 Test proposed by Alan
Turing in 1950
 The computer is asked
questions by a human
interrogator. It passes the
test if the interrogator
cannot tell whether the
responses come from a
person
 Required capabilities:
natural language processing,
knowledge representation,
automated reasoning,
learning,...

What would a computer need to pass
the Turing test?
• Natural language processing: to communicate with examiner.
• Knowledge representation: to store and retrieve information
provided before or during 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.
• 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.

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AI prehistory (The foundation of AI)
•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,
probability
•Economics decision theory
•Neuroscience physical substrate for mental activity
How do brain process information?
•Psychology phenomena of perception and motor control,
experimental techniques
How do humans and animals think and act?
•Computer engineering building fast computers
How can we build an efficient computer?
•Control theory design systems that maximize an objective
function over time
•Linguistics knowledge representation, grammar
How does language relate to thought?

Can Computers Talk?
Can Computers Recognize Speech?
Can Computers Understand speech?
Can Computers Learn and Adapt ?
Can Computers “see”?
What can computers do?

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Goals of AI
Replicate human intelligence
"AI is the study of complex information processing problems that often have their
roots in some aspect of biological information processing. The goal of the subject
is to identify solvable and interesting information processing problems, and solve
them." -- David Marr
Solve knowledge-intensive tasks
"AI is the design, study and construction of computer programs that behave
intelligently.” -- Tom Dean
Enhance human-human, human-computer and computer-
computer interaction/communication.
Computer can sense and recognize its users, see and recognize its environment,
respond visually and audibly to stimuli. New paradigms for interacting
productively with computers using speech, vision, natural language, 3D virtual
reality, 3D displays, more natural and powerful user interfaces, etc.

19
Some Application Areas of AI (What can IA do?)-
Game Playing.
Deep Blue Chess program beat world champion Gary Kasparov
Speech Recognition:
PEGASUS spoken language interface to American Airlines' EAASY SABRE
reservation system, which allows users to obtain flight information and make
reservations over the telephone. The 1990s has seen significant advances in
speech recognition so that limited systems are now successful .
Computer Vision:
Face recognition programs in use by banks, government, etc. Handwriting
recognition, electronics and manufacturing inspection, photo interpretation.

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Expert Systems:
Application-specific systems that rely on obtaining the knowledge of human
experts in an area and programming that knowledge into a system
.Diagnostic Systems-
Microsoft Office Assistant in Office 97 provides customized help by
decision-theoretic reasoning about an individual user. MYCIN system for
diagnosing bacterial infections of the blood and suggesting treatments.
Intellipath pathology diagnosis system (AMA approved). Pathfinder medical
diagnosis system, which suggests tests and makes diagnoses. Whirlpool
customer assistance center.
System Configuration-
DEC's XCON system for custom hardware configuration. Radiotherapy
treatment planning.

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Financial Decision Making–
Credit card companies, mortgage companies, banks, and the
U.S. government employ AI systems to detect fraud and
expedite financial transactions. For example, AMEX credit
check. Systems often use learning algorithms to construct
profiles of customer usage patterns, and then use these
profiles to detect unusual patterns and take appropriate
action .
Classification System-
Put information into one of a fixed set of categories using
several sources of information. E.g., financial decision
making systems. NASA developed a system for classifying
very faint areas in astronomical images into either stars or
galaxies with very high accuracy by learning from human
experts' classifications .

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Mathematical Theorem Proving–
Use inference methods to prove new theorems .
Natural Language Understanding-
AltaVista's translation of web pages. Translation of Catepillar Truck
manuals into 20 languages. (Note: One early system translated the
English sentence
Scheduling and Planning-
Automatic scheduling for manufacturing. DARPA's DART system
used in Desert Storm and Desert Shield operations to plan logistics
of people and supplies. American Airlines rerouting contingency
planner. European space agency planning and scheduling of
spacecraft assembly, integration and verification .

The AI Field…
Psychology
Philosophy
Logic
Sociology
Human Cognition
Linguistics
Neurology
Mathematics
Management Science
Information Systems
Statistics
Engineering
Robotics
Biology
Human Behavior
Pattern Recognition
Voice Recognition
Intelligent tutoring
Expert Systems
Neural Networks
Natural Language Processing
Intelligent Agents
Fuzzy Logic
Game Playing
Computer Vision
Automatic Programming
Genetic Algorithms
Machine Learning
Autonomous Robots
Speech Understanding
The AI
Tree
Computer Science
Disciplines
Applications
• AI provides the scientific foundation for many commercial
technologies

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Types of AI-
•Symbolic AI: Symbolic AI is based in logic. It uses sequences of rules to tell the
computer what to do next. Expert systems consist of many so-called IF-THEN rules:
IF this is the case, THEN do that. Since both sides of the rule can be defined in
complex ways, rule-based programs can be very powerful.
•Connectionist AI: Connectionism is inspired by the brain. It is closely related to
computational neuroscience, which models actual brain cells and neural circuits.
Connectionist AI uses artificial neural networks made of many units working in
parallel. Each unit is connected to its neighbours by links that can raise or lower the
likelihood that the neighbour unit will fire (excitatory and inhibitory connections
respectively). Neural networks that are able to learn do so by changing the strengths
of these links, depending on past experience. These simple units are much less
complex than real neurons. Each can do only one thing.
•Evolutionary AI: Evolutionary AI draws on biology. Its programs make random
changes in their own rules, and select the best daughter programs to breed the next
generation. This method develops problem-solving programs, and can evolve the
“brains” and “eyes” of robots. It is often used in modelling artificial life (A-Life).
Artificial Life (``AL'' or ``Alife'') is the name given to a new discipline that studies
"natural" life by attempting to recreate biological phenomena from scratch within
computers and other "artificial" media.

25
A Framework for Building AI Systems:
Perception -
Intelligent biological systems are physically embodied in the world and experience the
world through their sensors (senses). For an autonomous vehicle, input might be
images from a camera and range information from a rangefinder. For a medical
diagnosis system, perception is the set of symptoms and test results that have been
obtained and input to the system manually. Includes areas of vision, speech
processing, natural language processing, and signal processing (e.g., market data and
acoustic data(.
Reasoning
–Inference, decision-making, classification from what is sensed and what the internal
"model" is of the world. Might be a neural network, logical deduction system, Hidden
Markov Model induction, heuristic searching a problem space, Bayes Network
inference, genetic algorithms, etc. Includes areas of knowledge representation,
problem solving, decision theory, planning, game theory, machine learning,
uncertainty reasoning, etc .
Action–
Biological systems interact within their environment by actuation, speech, etc. All
behavior is centered around actions in the world. Examples include controlling the
steering of a Mars rover or autonomous vehicle, or suggesting tests and making
diagnoses for a medical diagnosis system. Includes areas of robot actuation, natural
language generation, and speech synthesis .

Bits of History
 1956: The name “Artificial Intelligence” is
coined
 60’s: Search and games, formal logic and
theorem proving
 70’s: Robotics, perception, knowledge
representation, expert systems
 80’s: More expert systems, AI becomes an
industry
 90’s: Rational agents, probabilistic reasoning,
machine learning
 00’s: Systems integrating many AI methods,
machine learning, reasoning under
uncertainty, robotics again

Stat-of-the-art
Some Achievements
 Computers have won over world
champions in several games, including
Checkers, Othello, and Chess, but still
do not do well in Go
 AI techniques are used in many
systems: formal calculus, video games,
route planning, logistics planning,
pharmaceutical drug design, medical
diagnosis, hardware and software
trouble-shooting, speech
recognition, traffic monitoring,
facial recognition,
medical image analysis, part
inspection, etc...
 Stanford’s robotic car, Stanley,
autonomously traversed 132 miles
of desert
 Some industries (automobile,
electronics) are highly robotized,
while other robots perform brain
and heart surgery, are rolling
on Mars, fly autonomously, …,
but home robots still remain
a thing of the future

State of the art
• 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
• Stanford vehicle in Darpa challenge completed autonomously a
132 mile desert track in 6 hours 32 minutes.

Course Overview
General Introduction
• 01 Introduction. [AIMA Ch 1] Why study AI? What is AI? The Turing
test. Rationality. Branches of AI. Research disciplines connected to
and at the foundation of AI. Brief history of AI. Challenges for the
future. Overview of class syllabus.
• 02 Intelligent Agents. [AIMA Ch 2] What is
an intelligent agent? Examples. Doing the right
thing (rational action). Performance measure.
Autonomy. Environment and agent design.
Structure of agents. Agent types. Reflex agents.
Reactive agents. Reflex agents with state.
Goal-based agents. Utility-based agents. Mobile
agents. Information agents.
sensors
effectors
Agent

Course Overview (cont.)
• 03 Problem solving and search. [AIMA Ch 3] Example:
measuring problem. Types of problems. More example
problems. Basic idea behind search algorithms.
Complexity. Combinatorial explosion and NP
completeness. Polynomial hierarchy.
• 04 Uninformed search. [AIMA Ch 3] Depth-first.
Breadth-first. Uniform-cost. Depth-limited. Iterative
deepening. Examples. Properties.
• 05-06 Informed search. [AIMA Ch 4] Best-first. A*
search. Heuristics. Hill climbing. Problem of local
extrema. Simulated annealing. Genetic Algorithms.
3 l 5 l
9 l
Using these 3 buckets,
measure 7 liters of water.
Traveling salesperson problem
How can we solve complex problems?

Course Overview (cont.)
Practical applications of search.
• 07-08 Game playing. [AIMA Ch 6] The
minimax algorithm. Resource limitations.
Alpha-beta pruning. Elements of
chance and non-
deterministic games.
tic-tac-toe

Course Overview (cont.) - Learning
• 09 Learning [AIMA Ch 18]
Decision trees. Learning
decision trees. Inferring
from examples. Noise and
overfitting.
• 10 Neural Networks
[AIMA Ch 20]
Introduction to
perceptrons, How to size a
network? What can neural
networks achieve?
• 11 Learning 3
[AIMA Ch 19]
Current best hypothesis.
Maybe case-based and
analogical learning.
x (t)
1
x (t)
n
x (t)
2
y(t+1)
w1
2
n
w
w
axon


Main Areas of AI
 Knowledge representation
(including formal logic)
 Search, especially
heuristic search (puzzles,
games)
 Planning
 Reasoning under
uncertainty, including
probabilistic reasoning
 Learning
 Agent architectures
 Robotics and perception
 Natural language
processing
Search
Knowledge
rep.
Planning
Reasoning
Learning
Agent
Robotics
Perception
Natural
language
... Expert
Systems
Constraint
satisfaction

EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.ppt

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