This document provides information about an Artificial Intelligence course. The key details are: - The course is CSC 343, taught over 3 lecture hours and 2 lab hours

2. Artificial Intelligence
Course No.: CSC 343
Lect.: 3 h
Lab. : 2 h
Marks: 65 final
10 Y. work
25 Lab+ Oral
Exam hours: 3 h
By Prof. Dr. :
Taymoor M. Nazmy

3. The text book
Russell & Norvig,
"Artificial Intelligence:
A Modern Approach",
2nd Edition, Prentice Hall,
2003.

4. Course Objective
A broad introduction and appreciation of Artificial
Intelligence and its applications.
Course Outline
Definition of Artificial Intelligence. Modelling
intelligence: adaptive and knowledge based
approaches to analysis and design of intelligent
systems. Application domains of Artificial
Intelligence. Types of Artificial Intelligence systems.

5. Course Content
Introduction
Rational Agents
Search and Problem
solving
Informed search
Propositional logic
Predicate logic
Knowledge representation
using logic
Planning
Probabilistic Reasoning
Planning under
uncertainty
Machine Learning
Reinforcement learning
Neural Networks
Natural Language
Processing

7. What is Artificial
Intelligence?
definitions
Turing test
rational thinking
acting rationally
Foundations of Artificial
Intelligence
philosophy
mathematics
psychology
computer science
linguistics
History of Artificial
Intelligence
Applications of AI

8. -select a task that you believe requires intelligence
examples:
-playing chess, solving puzzles, translating from English to
German, finding a proof for a theorem.
-for that task, sketch a computer-based system that tries to
solve the task architecture, components, behavior.
what are the computational methods your system relies on-
e.g. data bases, matrix multiplication, graph traversal.
what are the main challenges?
how do humans tackle the task?

9. Foundations of AI
Computer
Science &
Engineering
AI
Mathematics
Cognitive
Science
Philosophy
Psychology Linguistics
BiologyEconomics

10. Foundations of AI
Engineering:
robotics, vision, control-expert systems, biometrics,
Computer Science:
AI-languages , knowledge representation, algorithms, …
Pure Sciences:
statistics approaches, neural nets, fuzzy logic, …
Linguistics:
computational linguistics, phonetics , speech, …
Psychology:
cognitive models, knowledge-extraction from experts, …
Medicine:
human neural models, neuroscience,...

11. Fundamental Issues for most AI problems
1- Representation
Facts about the world have to be represented in some
way, e.g., mathematical logic is one language that is
used in AI.
Deals with the questions of what to represent and how
to represent it. How to structure knowledge? What is
explicit, and what must be inferred? How to encode
"rules" for inferencing so as to find information that is
only implicitly known?
How to deal with incomplete, inconsistent, and
probabilistic knowledge?

12. 2-Search
Many tasks can be viewed as searching a very large
problem space for a solution. For example, Checkers
has about 1040 states, and Chess has about 10120
states in a typical games. Use of heuristics (meaning
"serving to aid discovery") and constraints.
3-Inference
From some facts others can be inferred. Related to
search. For example, knowing "All elephants have
trunks" and "Clyde is an elephant," can we answer
the question "Does Clyde have a trunk?"
Deduction, abduction, reasoning under uncertainty.

13. 4-Learning
Inductive inference, neural networks, genetic algorithms,
evolutionary approaches.
5-Planning
Starting with general facts about the world, facts about
the effects of basic actions, facts about a particular
situation, and a statement of a goal, generate a strategy
for achieving that goals in terms of a sequence of
primitive steps or actions.

14. “AI develops programming paradigms, languages,
tools, and environments for application areas for which
conventional programming fails”, such as:
Symbolic programming (LISP)
Logical Programming (PROLOG)
Rule-based Programming (Expert system shells)
Soft Computing (Belief network tools, fuzzy logic
tool boxes,…)
Object-oriented programming (Smalltalk)

15. What is AI?

16. What is AI?
Many definitions, most fit into one of four categories:
Systems that act humanly
Systems that think humanly
Systems that act rationally
Systems that think rationally

17. Systems that think like humans
Cognitive science
Fascinating area, but we will not be covering it in
this course.
Systems that think rationally
Aristotle: What are the correct thought processes
Systems that reason in a logical manner
Systems doing inference correctly.
Systems that act rationally
(Rational behavior ) Doing the right thing
Rational agent approach
Agent: entity that perceives and acts
Rational agent: acts so to achieve best outcome

18. Turing in 1950 published a philosophical paper designed to
stop people arguing about whether or not machines could
think. He proposed that the question be replaced with a test.

19. Acting Rationally
Acting rationally means that one acts to achieve
his/her goals given his/her beliefs.
AI can be viewed as the study and creation of rational
agents. An agent is something that can perceive
and act.
The study of AI as rational agent design has two
advantages:
(a) it is more general than the “laws of thought”
approach, and
(b) it is more amenable to scientific development
than approaches that limit themselves to human
behavior or human thought.

24. History

25. Examples of Modeling Human Intelligence
Semantic networks are designed after the
psychological model of the human
associative memory.
John Plumber Person
Owner Ford Car
May 97 Time
Oct 00
Ownership Situation
Is a Is a
Is a
Is a
Is a
Is a
Owner
Ownee
Start-time
End-time
Ford
Is a

26. Modeling Human Intelligence
Rule-based or Expert systems - Knowledge bases
consisting of hundreds or thousands of rules of the form:
IF (condition) THEN (action).
Use rules to store knowledge (“rule-based”).
The rules are usually gathered from experts in the field
being represented (“expert system”).
Most widely used knowledge model in the commercial world.
IF (it is raining AND you must go outside)
THEN (put on your raincoat)
Rules can fire off a chain of other rules
IF (raincoat is on)
THEN (will not get wet)

27. Expert Systems
Expert systems were commercially the most
successful domain in Artificial Intelligence.
Somewhat out of favor today
These programs mimic the experts in whatever
field.
Auto mechanic Telephone networking
Cardiologist Delivery routing
Organic compounds Professional auditor
Mineral prospecting Manufacturing
Infectious diseases Pulmonary function
Diagnostic internal medicine Weather forecasting
VAX computer configuration Battlefield tactician
Engineering structural analysis Space-station life support
Audiologist Civil law

28. Expert Systems
Two major parts of an expert system:
The knowledge base: The collection of rules
that make up the expert system.
The inference engine: A program that uses the
rules by making several passes over them.
On each pass, the inference engine looks for all
rules whose condition is satisfied (if part).
It then takes the action (then part) and makes
another pass over all the rules looking for matching
condition.
This goes on until no rules’ conditions are matched.
The results are all those action parts left.

29. Human Brain and Neural Networks
-Human Brain is made up of Billions of cells called
neurons,
- Neurons work when grouped together Decisions
are made by passing electrical signals,
- Neurons are devices for processing Binary digits,
-A neuron: many-inputs / one-output unit,
-output can be excited or not excited,
-incoming signals from other neurons determine if
the neuron shall excite ("fire")
Output subject to attenuation in the synapses, which
are junction parts of the neuron

30. Artificial Neural Network: A collection of neurons
which are interconnected. The output of one connects to
several others with different strength connections.
Initially, neural networks have no knowledge. (All
information is learned from experience using the
network.)
Input 1
Input 2
Input 3
Neuron 1
Neuron 2
Output from
Neuron 1
Output from
Neuron 2
Artificial neural network
Natural NN

31. Comparing the computer with the human brain

32. Evolutionary Systems
Genetic Programming:
A technique that follows Darwinian evolution.
The evolution takes place directly on the
programs in the population that are striving
to reach the goal specified by the
programmer.
Only the goal is known and possibly some
of the structure of the solution..

33. The concept of modern approach of AI
(Perception and action AI agent)
Organisms in the real world have gather information about
their
environment (perception) and
based on this information, they have to manipulate
their environment (including themselves) in a way
that is advantageous to them (action).
The action in turn may cause a change in the organism’s
perception, which can lead to a different type of action.
We call this the perception-action cycle.
Complex organisms do not just perceive and act, but they also
have an internal state that changes based on the success of
previous perception-action cycles.
This is the mechanism of learning.

34. AI “Application” Areas
Rule-Based Expert Systems
Medical Diagnosis: MYCIN, INTERNIST, PUFF
CSP Scheduling: ISIS, Airline scheduling
Data Mining
Financial: Fraud detection, credit scoring
Sales: Customer preferences, inventory
Science: NASA galaxy DB, genome
analysis

35. AI “Application” Areas (cont.)
Language Processing
Speech: dictation, HCI
Language: Machine Translation
ML & NLP: Fact Extraction
ML & words: Information Retrieval
Robotics
Machine Vision
Mobile Robots & “agents”
Manipulation

36. Applications
Game playing : Chess, Draughts,..
Speech recognition :speech to word processors
Understand natural language: understand meaning
of a whole sentence
Computer vision : 3-D world, but human eye and
camera are 2
Expert systems: stored knowledge
Robotics: space missions
Character recognition :handwriting
Pattern recognition: Faces, fingerprint,