CSC-470
ARTIFICIAL INTELLIGENCE
Lecturer
Muhammad Tariq Siddique
https://sites.google.com/site/mtsiddiquecs/ai
About This Course
 Course Code: CSC-470
 Course Title: Artificial Intelligence
 Credit Hours: 3 (2+1)
 Abbreviation: AI
 Prerequisite: none
 Type of Course: Core Course
 Course Description:
Introduction, Search, Informed Search, CSP,
Game Playing, Logic, Planning, Machine
Learning, LISP, Prolog, Neural Networks, Back
Propagation, Self Organizing Maps, Radial basis
function networks, Expert Systems.
Lecture & Lab Section
Lecture #1:
Tuesday 09:30-11:25
Room LT-3
Lab #1:
Friday 02:30-05:25
Lab-6
Course Assessment
Final Examination
Midterm Examination
Assignments/Project
Quizzes
Total
Asgns
20%
Midterm
20%
Quizzes
10%
50%
20%
20%
10%
100%
Scoring
Final
50%
Text Book and Reference Books
Text Book
 S. Russell, Artificial Intelligence: A Modern
Approach, Prentice Hall, (3rd edition)
Reference books
 Ben Coppin, “Artificial Intelligence
Illuminated”, Jones and Bartlett illuminated
Series, 2004
 Simon Haykin, “Neural Networks: A
Comprehensive Foundation”, Prentice Hall,
1999
Introduction to AI
 Human beings have intelligence and they are named as
homo sapiens - man the wise
 Main objectives are
to model and process intelligence so as to build
intelligent entities (Systems).
Scientific motivations - Science of mind
Approaches
 Symbolic Computation,
 Neural Computing
 Evolutionary Computing
Emphasis in the course will be the first approach
with some mention of other approaches
What is AI ?
thinking like humans: decision-making, problem
solving, learning
thinking rationally: study of mental faculties
through computational models
acting like humans: perform functions requiring
intelligence of humans
acting rationally: automation of intelligent behavior
Some definitions of AI
R&N’s Matrix:
Thinking Humanly
(Cognitive Sciences)
Thinking Rationally
(Idealized Logic)
Acting Humanly
(Turing Test)
Acting Rationally
(The Agent Approach)
Is there really a difference between the right two
boxes, given that it’s possible to understand ‘acting’
so that ‘thinking’ is acting?
The Turing Test
Figure 13.2
In a Turing test, the
interrogator must
determine which
respondent is the
computer and which is
the human
Turing Test
 Turing (1950) “Computing machinery and intelligence”.
 A computer passes the test if a human interrogator, after posing
some written questions, cannot tell whether the written
responses come from a person or from a computer.
 Little effort by AI researchers to pass the Turing Test
Turing Test
Major Components of Turing Test:
Natural Language Processing: To enable it to communicate
successfully in English.
Knowledge Representation: To store what it knows or hears.
Automated Reasoning: To use the stored information to
answer questions and to draw conclusions.
Machine Learning: To adapt to new circumstances and to
detect and extrapolate patterns.
Total Turing Test also includes:
Computer Vision: To perceive objects
Robotics: To manipulate objects and move about
Thinking humanly
Thinking humanly : cognitive modeling
approach
Try to understand a person’s thoughts
Cross-discipline: computer models from AI and
experimental techniques from psychology
Aim: to construct exact and testable theories of
how the mind works
Thinking rationally
 Thinking rationally: The laws of thought approach
Irrefutable (undisputable) reasoning processes/logic
Syllogisms – patterns for argument structures
(premise(s) leading to conclusion)
• E.g. “Socrates is a man; All man are mortal; therefore
Socrates is mortal.”
Problems:
• Not easy to take informal knowledge and put it in
logical terms
• Different between solving in principle and solving in
practice
Acting rationally
Acting rationally: The rational agent approach
An agent perceives and acts. Hence, emphasis is
on correct inferences
Problem:
to infer correctly is sometimes not possible as
sometimes, there is no one correct conclusion
Sometimes, an action is not the result of
thought (e.g. reflex action)
Acting rationally
Require cognitive skills similar to Turing test:
Means to represent knowledge in order to enable
reasoning which results in good decisions in many
situations
Easy to understand language by using natural
language processing
Good idea of what is practiced in real world so that
strategies are more effective
Visual perception to perceive objects in order to act
according to changes in situations
Acting rationally
Benefits of rational agent approach
More general than the laws of thought approach
• Correct inference is a useful mechanism but not a
necessarily so
Better for scientific development than acting humanly
and thinking humanly approaches because the
standard of rationality is clearly defined and totally
general
• Human behavior differs according to situations
Note: achieving total rationality is not possible – why?
Solution?
Approached to Artificial Intelligence
Search
Learning
Rule-Based Systems Search
Reasoning (logic)
Planning
Ability-Based Areas
Robotics
Agent
Search
 “All AI
is search”
Game theory
 Problem spaces

 Every problem is a “virtual” tree of all
possible (successful or unsuccessful)
solutions.
 The trick is to find an efficient search
strategy.
Learning
Explanation
Discovery
Data Mining
No Explanation
Neural Nets
Case-Based Reasoning
Rule-Based Systems
Logic Languages
Prolog, Lisp
Knowledge bases
Inference engines
Planning: Game Theory
•
Game Playing
• Mutliagent Environment
• Deterministic
• Perfect/imperfect information
•
Examples
• Chess
• Nuclear war
• Poker
Ability Based Areas
Computer vision
Natural Language Processing
Speech recognition
Speech generation
NLP

Natural language processing (NLP) is a subfield
of artificial intelligence and syntax. It studies the
problems of automated generation and
understanding of natural human languages.

Natural language generation systems convert information
from computer databases into normal-sounding human
language, and natural language understanding systems
convert samples of human language into more formal
representations that are easier for computer programs to
manipulate.
Computer Vision
The technology concerned
with computational
understanding and use of
the information present in
visual images.
 In part, computer vision is
analogous (similar) to the
transformation of visual
sensation into visual
perception in biological
vision.

Robotics
ro·bot
A mechanical device that sometimes
resembles a human and is capable of
performing a variety of often complex
human tasks on command or by being
programmed in advance.
 A machine or device that operates
automatically or by remote control.
 A person who works mechanically without
original thought, especially one who
responds automatically to the commands of
others.

Weak and Strong AI Claims
Weak AI:
Machines can be made to act as if they were
intelligent.




Weak A.I. refers to A.I. that only simulates human thoughts and actions.
Actions, decision and ideas are programmed into it.
They mimic humans based on their programming
All current forms of A.I. are ‘Weak A.I.’
Strong AI:
Machines that act intelligently have real, conscious
minds.





Strong A.I. refers to A.I. that matches or exceeds human intelligence.
Example: The robots from the movies ‘Matrix, Terminator, iRobot, Artificial Intelligence’.
Also called “True A.I.”, as they are truly intelligent.
They don’t just simulate humans, they are intelligent on their own.
Able to learn freely and adapt, self aware, free will.
Examples
Intelligent Systems in Your Everyday Life
 Post office
automatic address recognition and sorting of mail
 Banks
automatic check readers, signature verification systems
automatic loan application classification
 Telephone Companies
automatic voice recognition for directory inquiries
automatic fraud detection
classification of phone numbers into groups
 Credit Card Companies
automated fraud detection , automated screening of applications
 Computer Companies
automated diagnosis for help-desk applications
The Foundation of Artificial Intelligence
Philosophy
Psychology
Mathematics
Computer Engineering
Linguistics
AI Tree..
Affective computing
Machine Learning
Automatic
Programming
Speech Understanding
Robotic
Natural Language
Processing
Game Playing
Neural Network
Expert System
Fuzzy Logic
Intelligent Tutor
Genetic Algorithm
Computer Vision
Data Mining
Linguistics
Computer Science
Psychology
Philosophy
Management &
Management Science
Electrical Engineering
Relevance Tree For Artificial Intelligence
The History of Artificial Intelligence
 Ancient Times
 Aristotle (Logic as an instrument for studying thought)
 Early Efforts by Philosopher/Mathematicians
 Descartes (The mind body problem)
 Hume (Cognition is computation)
 Euler (1735 - representation of structure; search)
 Formal Notation and Calculating Machines
 Leibnitz (1887 - first system of formal logic; calculating machines)
 Babbage (Programmable computing machines)
 Boole (1847, 1859)
 Frege (1879, 1884 - first-order predicate calculus)
The History of Artificial Intelligence
(Continued …)
Modern Founders of AI
Alan Turing ("Computing Machinery and
Intelligence"; Turing test) (1950)
McCulloch & Pitts (neural nets) (1943)
Norbert Wiener (cybernetics)
John von Neumann (game theory)
Claude Shannon (information theory)
Newell & Simon (The Logic Theorist)
John McCarthy (LISP, commonsense reasoning)
Marvin Minsky (Frames)
Donald Michie (Freddy)
The History of Artificial Intelligence (Con’t …)
 Achievements of AI
Deep Thought is an international grand master chess player.
Sphinx can recognise continuous speech without training for
each speaker. It operates in near real time using a
vocabulary of 1000 words and has 94% word accuracy.
Navlab is a car that has driven across the United States at
55mph in normal traffic on freeways.
Carlton and United Breweries use an AI planning system to
plan production of their beer.
Robots are used regularly in manufacturing.
Natural language interfaces to databases can be obtained on
a PC.
Machine Learning methods have been used to build expert
systems.
Expert systems are used regularly in finance, medicine,
manufacturing and agriculture
The History of Artificial Intelligence (Con’t …)
 1943-1956 (the age of invention)
◦ McCulloch & Pitts/Hebb (a model of brain)
 Simple neural models of processing
◦ Claude Shannon/Turing
 computers can manipulate symbols
 chess as a canonical (official) example
◦ Dartmouth workshop (1956)
 Origin of the term “AI”
◦ Newell &Simon
 Logic Theorist
 1952-1969 (Early AI Programs)
◦ General Problem Solver
◦ LISP (1958)
◦ Resolution method (1965)
AI Today
 Robotics:
automated driving
museum guides
 Natural Language
speech systems now a commercial product
Translate spoken English into spoken other languages
 Vision
commercial systems in the offing for recognition
industrial inspection a big industry
 Data Mining and knowledge discovery
Reading Assignments
 What is AI? Foundations of AI and history. Read Chapter 1
from Russell & Norvig