1. Artificial Intelligence (CS 370D)
Princess Nora University
Faculty of Computer & Information Systems
Artificial Intelligence (CS 370D)
L.Rana AlEkresh
Computer science Department

2. Course Title: Artificial Intelligence Code : CS 461, CS 370D
Prerequisites: CS 220D + CS 111 D
Credits: ( lecture lab )= 3

3. Topics
This course will introduce you to the field of Artificial Intelligence
Main topics which will be covered:
Introduction to AI.
Intelligent Agents
Searching(Uninformed search , informed search )
Constraint Satisfaction Problems(CSP)
Introduction to game theory
First order logic (Syntax and semantics )

4. Topics (cont..) Knowledge representation
Forms of learning (Inductive, deductive)
Programming in logic PROLOG
Graph representation
Robotics (overview)

5. Course Aims Give you an understanding of what AI is
Aims, abilities, methodologies, applications, …
Equip you with techniques for solving problems
By writing/building intelligent software/machines

6. Goals By the end of the course the students should be able to:
Understand the fundamental concepts of Artificial Intelligence
Understand different methods of search and optimization in AI
Able to develop small application using heuristic functions to solve any search problem in AI
Understand the learning strategies
Understand and implement searching techniques
Understand the fundamental concept of logic in AI
Understand the knowledge areas
Learn PROLOG language

7. Books and references: Main Text Books:
“Artificial Intelligence – A Modern Approach” , Stuart Russel and Peter Norvig: 3rd Edition Pearson
Ivan Bratko :PROLOG Programming 2nd Ed., Pearson Education
“Artificial Intelligence “, Elaine Rich and Kevin Knight: 2nd Ed , Tata McGraw Hill

8. Resources
Course :

9. Percentage from overall grade
Marks Distribution
Percentage from overall grade
Grade
Assessment Week
Assessment method (Write an essay - test - a collective project - a final test ...)
15% 15
6th week
1st Med Term
11th week
2nd Med Term
10% 10
Quiz +Homework
week Assignments 2rd, 4th, 6th , 8th and 10th + Attendance
(Lab)
exam 40
After 15
Final exam (Theory) “Two academic hours“.
100
Total
- Marks distribution is not final and is subject to change.

10. Course Policy + agree on the excuse from department
NO bonus
NO makeup quizzes.
NO midterm makeup exams unless:
You must bring a medical excuses from
a government hospital.
+ agree on the excuse from department
+ agree on the excuse from course coordinator
Then the makeup exam will be in the whole course contents

11. Course Policy (cont..)
Assignments must be completed individually unless specified otherwise.
If groups are permitted, each student should team up with students from the same tutorial section.
Cheating is forbidden. Both parties will be penalized in Minus.

12. Course Policy (cont..) Email Communication:
Anonymous s will be ignored.
When you send an , you should use your PNU account
make sure to put “AI 370D or AI 461D" in the subject line and
identify yourself with your group code and Student ID in the message (body).
Late submissions of any course material is not allowed.
It is your responsibility to check the course’s website regularly for any assignments, announcements, etc..

13. Thank you
Enjoy the Course
& never forget to smile 

14. (Chapter-1) Introduction

15. Chapter 1: Introduction
What is AI ?
Applied Areas of AI.
What’s involved in Intelligence?

16. OBJECTIVES:
explain why we consider artificial intelligence (AI) to be a subject most worthy of study,
decide what exactly AI,

17. Why Would You Study Artificial Intelligence? (1)
Artificial intelligence impact on society is growing rapidly:
in speech and language technology,
strategic planning and
diagnosis,
process and system control,
vision and authentication systems,
information retrieval and
data-mining and many other contexts.

18. Why Would You Study Artificial Intelligence? (2)
knowledge is power
Due to the rapidly expanding role of AI in our current and future society, there is an urgent need for academically trained people with the variety of backgrounds who are familiar with the fundamentals of AI, aware of its reasonable expectations, and have practical experience in solving AI problems

19. Roots of artificial intelligence (1)
PHILOSOPHY (428 b.c. -present)
Can formal rules be used to draw valid conclusions?
Where does knowledge come from?
How does knowledge lead to action?
MATHEMATICS (c present)
What are the formal rules to draw valid conclusions? (formal logic)
What can be computed? (algorithms)
How do we reason with uncertain information? (probability theory, fuzzy sets, etc.)

20. Roots of artificial intelligence (2)
ECONOMICS (1776 -present)
How should we make decisions so as to maximize payoff?
NEUROSCIENCE (1861 -present)
How do human brains process information? (neural networks)
PSYCHOLOGY (1879 -present)
How do humans and animals think and act? (behaviorism, cognitive psychology, cognitive science)

21. Roots of artificial intelligence (3)
COMPUTER ENGINEERING (1940 -present)
How can we build an efficient computer?
CONTROL THEORY AND CYBERNETICS (1948 -present)
How can artifacts (man made objects) operate under their own control? (automatic)
•LINGUISTICS (1957 -present)
How does language relate to thought?
(natural language processing, knowledge representation)

22. Basic Questions

23. Q. 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.

24. Q. What’s involved in Intelligence? (1)
Reasoning
الاستنتاج
Inference
الاستدلال
Perception
الادراك
Learning
التعلم
Knowledge-Based
تعتمد علي المعرفة
Problem Solving
حل المشاكل
Non-algorithmic
لا يوجد خطوات للحل

25. Q. What’s involved in Intelligence? (2)
Ability to interact with the real world
to perceive, understand, and act
e.g., speech recognition and understanding and synthesis
e.g., image understanding
e.g., ability to take actions, have an effect
Reasoning and Planning
modeling the external world, given input
solving new problems, planning, and making decisions
ability to deal with unexpected problems, uncertainties
Learning and Adaptation
we are continuously learning and adapting
our internal models are always being “updated”
e.g., a baby learning to categorize and recognize animals

26. Q. What is artificial intelligence definition?

27. Artificial Intelligence models human behavior
Problem-Solving
Expert Systems
Sight
Vision
Hearing , Speech
Natural Language Understanding
Robotics
Motor Function
Tutoring Systems
Education 27

28. Q. What is artificial intelligence definition? -1
To day we still don’t have an unambiguous and comprehensive definition of artificial intelligence
definition(1) :
AI is that branch of science which makes machines perform tasks which would require intelligence when performed by humans (Marvin Minsky)
definition(2) :
is the study of the formal properties of problems and problem-solving methods, with the aim of equipping computers with problem-solving capabilities that are comparable to those of a human being

29. Logically or based on reason
Actually definitions of artificial intelligence can be grouped in main categories:
Logically or based on reason

30. The previously mentioned categories of definitions can be considered along 2 dimensions:
Definitions related to thinking processes and reasoning
Definitions related to behavior

31. The other dimensions can be the following:
Definitions that evaluate a success of an intelligent artificial system in terms of human action/operation/ performance
Definitions that evaluate a success of an intelligent artificial system in terms of an ideal intelligence called rationality

32. SYSTEMS THAT THINK LIKE HUMANS
If we want to develop a computer that thinks like humans we need to know how people think
Cognitive science integrates computational models developed in the area of artificial intelligence with techniques from psychology in order to develop the ories about how the human mental mind works

33. SYSTEMS THAT THINK RATIONALLY
This approach is related to logics, that is ,logical rules make the mental mind of humans
For example, if we know that All people have ahead and Alexis one of people, than we can conclude that Alex has ahead

34. SYSTEMS THAT ACT LIKE HUMANS
In this approach computer capabilities are compared with human capabilities
For this purpose a special test of intelligent behavior is defined. The test is called the Turing test
The idea of the test is the following. There are 3 rooms.
In the first one there is an artificial intelligence,
in the second room-a person or natural intelligence,
and in the third room there is a tester.
The tester asks questions to both intelligences.
If it is impossible to determine which answers were given by the person and which ones by the machine, than the machine has intelligence

35. In a Turing test, the interrogator must determine which respondent is the computer and which is the human

36. SYSTEMS THAT ACT LIKE HUMANS (cont..)
To pass the Turing test the computer must have the following capabilities:
Natural language processing
Knowledge representation
Automated reasoning
Machine learning
However ,the Turing test excludes direct physical contact between the machine and the tester. The so called the Total Turing test brings forward two more requirements:
computer vision in order to perceive objects, and
Robotics in order to move objects

37. SYSTEMS THAT ACT RATIONALLY
A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome
Rational behavior : doing the right thing
The right thing which is expected to maximize goal achievement given the available information

38. Research directions of artificial intelligence
Today there are 2 main research directions in artificial intelligence:
(1) BIONICS: approaches that have focus on humans and based on empirical knowledge acquired during different experiments
(2) PRAGMATIC DEVELOPMENT OF COMPUTER PROGRAMS: approaches based on rationality and combining mathematics and computer engineering

39. Research directions of artificial intelligence
There are two main lines of research.
One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology.
The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of goals.

40. Some real applications

41. Applied Areas of AI Game playing Speech and language processing
Expert reasoning
Planning and scheduling
Vision
Robotics

42. Autonomous Intelligent Systems
Mobile robots
Flying vehicles
Adaptive techniques and
learning
Multi-robot systems
Applications of mobile robots
Interaction and Web interfaces

43. Humanoid Robots Resource-constrained systems
Perception, state estimation
3D environment modeling
Path planning and navigation in cluttered
environments
Natural human-robot interaction
Human motion analysis
Imitation of human motions

44. Social Robotics Towards socially compatible robots
Social learning, learning by observation
People detection and tracking
Motion planning
Robot navigation
Spatio-temporal models of human
social behavior
Human-robot interaction
"Free robots from their social isolation"

45. Cognitive Cognitive models of human thinking, reasoning, and planning
Qualitative Reasoning and imprecise
knowledge
Cognitive complexity analysis
Behavioural and fMRI experiments
Systems that solve IQ-test problems
\Build systems that reason and plan
like humans"

46. Some Examples Playing chess Driving on the highway
Translating languages
Recognizing speech
Diagnosing diseases

47. Playing Chess Environment? Board Actions? Legal moves
Doing the right thing?
Moves that lead to wins

48. Recognizing Speech Environment Audio signal Knowledge of user Actions
Choosing word sequences
Doing the right thing
Recovering the users words

49. Diagnosing Diseases Environment Patient information Results of tests
Actions
Choosing diseases
Choosing treatments
Doing the right thing
Eliminating disease

50. Translation Environment Source text to be translated Actions
Word sequences in target language
Doing the right thing?
Words that achieve the same effect
Words that are faithful to the source

51. Driving Environment Restricted access highway Actions
Accelerate, turn, navigate, other controls
Doing the right thing
Stay safe, get where you want to go, get there quickly

52. (Chapter-2) Intelligent agents

53. Lecture 2 Rational Agents What is an Agent? What is a rational agent?
The structure of rational agents
Different classes of agents
Types of environments

54. What is an Agent? Perceive the environment through sensors (Percepts)
Act upon the environment through actuators (Actions)

55. We use the term percept to refer to the agent ‘s perceptual inputs at any given instant
An agent percept sequence is the complete history of every thing the agent has ever perceived.
The agent function maps from percept histories to actions:
[f: P*  A]
Is there a difference between agent function and agent program?

56. Is there a difference between agent function and agent program?
The agent function will internally be represented by the agent program.
The agent program runs on the physical architecture to produce f

57. Agents Human agent: eyes, ears, and other organs for sensors;
hands, legs, mouth, and other body parts for
actuators
Robotic agent:
cameras and infrared range finders for sensors; various motors for actuators

58. The vacuum-cleaner world (1)
Environment: square A and B
Percepts: [location and content] e.g. [A, Dirty]
Actions: left, right, suck, and no-op
Agent’s function  look-up table

59. The vacuum-cleaner world (2)
Percept sequence
Action
[A,Clean]
Right
[A, Dirty]
Suck
[B, Clean]
Left
[B, Dirty]

60. The vacuum-cleaner world (3)
function REFLEX-VACUUM-AGENT ([location, status]) return an action
if status == Dirty then return Suck
else if location == A then return Right
else if location == B then return Left

61. 2. Rational agent? . . . do the \right thing"!
In order to evaluate their performance, we have to define a performance measure.
Autonomous vacuum cleaner example:
m2 per hour
Level of cleanliness
Energy usage
Noise level
Safety (behavior towards hamsters/small children)
Optimal behavior is often unattainable(not totally achieved)
Not all relevant information is perceivable
Complexity of the problem is too high

62. Rationality vs. Omniscience
An omniscient agent knows the actual effects of its actions and it is impossible in real world
In comparison, a rational agent behaves according to its percepts and knowledge and attempts to maximize the expected performance
Example: If I look both ways before crossing the street, and then as I cross I am hit by a car, I can hardly be accused of lacking rationality.
Omniscient =have all the knowledge

63. The Ideal Rational Agent
Rational behavior is dependent on
Performance measures (goals)
Percept sequences up to date
The agent prior Knowledge of the environment
Possible actions
Active perception is necessary to avoid trivialization.
The ideal rational agent acts according to the function
Percept Sequence X World Knowledge Action

64. Task Environment
Before we design an intelligent agent, we must specify its “task environment”:
PEAS:
Performance measure
Environment
Actuators
Sensors

65. PEAS Example: Agent = robot driver in DARPA Challenge
Performance measure:
Time to complete course
Environment:
Roads, other traffic, obstacles
Actuators:
Steering wheel, accelerator, brake, signal, horn
Sensors:
Optical cameras, lasers, sonar, accelerometer, speedometer, GPS, engine sensors,

66. Examples of Rational Agents
Agent Type
Performance
Measure
Environment
Actuators
Sensors
Medical
diagnosis
system
healthy patient,
costs, lawsuits(court cases)
patient,
hospital, stuff
display questions,
tests, diagnoses,
treatments,
keyboard entry
of symptoms,
findings,
patient's answers
Satellite
image analysis
correct image
categorization
downlink from
orbiting satellite
display
of scene
color pixel
arrays
Part-picking
robot
percentage of
parts in correct bins(box)
conveyor belt
with parts, bins
jointed arm
and hand
camera, joint
angle sensors
Refinery
controllerمعمل تكرير
purity, safety
Refinery,
operators
valves pumps,
heaters displays
temperature,
pressure,
chemical sensors
Interactive
English tutor
student's score
on test
set of students,
testing agency
display exercises,
corrections
web crawling agent
did you get only pages you wanted
User, internet
Display related info

67. Environment types (1) Fully observable (vs. partially observable):
An agent's sensors give it access to the complete state of the environment at each point in time.
An environment might be partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data

68. Environment types (2) Deterministic (vs. stochastic):
The next state of the environment is completely determined by the current state and the action executed by the agent.
If the environment is deterministic except for the actions of other agents, then the environment is strategic
Vacuum cleaner is Deterministic why? Environment is fully known
Taxi driving agent (robot driving agent) is stochastic, why?
He doesn’t know about traffic, can never predict traffic situation

69. Environment types (3) Episodic (vs. sequential):
An agent’s action is divided into atomic episodes.
Each episodic perceive then take action
and next episodic does not rely on previous one it taking the right action.

70. Environment types (4) Static vs. dynamic:
Static environment is unchanged while an agent is deliberating. • Static environments are easy to deal with because the agent need not keep looking at the world while it is deciding on the action or need it worry about the passage of time • Dynamic environments continuously ask the agent what it wants to do • Examples: taxi driving is dynamic, chess when played with a clock is semi-dynamic, crossword puzzles are static

71. Discrete vs. continuous:
Environment types (5)
Discrete vs. continuous:
A limited number of distinct, clearly defined states, percepts and actions.
Examples:
Chess has finite number of discrete states, and has discrete set of percepts and actions.
Taxi driving has continuous states, and actions

72. Single agent vs. multiagent:
Environment types (6)
Single agent vs. multiagent:
• An agent operating by itself in an environment is single agent
• Examples:
Crossword is a single agent
while chess is two-agents
chess is a competitive multiagent environment
while taxi driving is a partially cooperative multiagent environment

73. Environment types (7) The simplest environment is
Fully observable, deterministic, episodic, static, discrete and single-agent.
Most real situations are:
Partially observable, stochastic, sequential, dynamic, continuous and multi-agent.

74. 3.Structure of Rational Agents
Realization of the ideal mapping through an
Agent program, executed on an
Architecture which also provides an interface to the environment (percepts, actions)

75. Basic kind of agent Programs
Reflex agents
Simple reflex agents
Model based Reflex agents
Goal Based agents
Utility Based agents
Learning agents

76. 1.1- Simple Reflex Agent
Select actions on the basis of the current percept, ignorig the rest of the percept history
Ex vacuum cleaner , why?
Because its decision based only on the current location and whether it contain dirt or not.

77. 1.2- Model-based Reflex Agents
The most effective way to handle partial observably
the agent's history in perception in addition to the actual percept is required to decide on the next action, it must be represented in a suitable form.

78. 2- Goal-based Agents
Often, percepts alone are insufficient to decide what to do.
This is because the correct action depends on the given explicit goals (e.g., go towards X).
The goal-based agents use an explicit representation of goals and consider them for the choice of actions.
Ex taxi driving destination , vacuum cleaner

79. 3- Utility-based Agents
Usually, there are several possible actions that can be taken in a given situation.
In such cases, the utility of the next achieved state can come into consideration to arrive at a decision.
A utility function maps a state (or a sequence of states) onto a real number.
The agent can also use these numbers to weigh the importance of competing goals.
Ex taxi driving , may be many paths lead to goal but some are quicker, cheaper, safer

80. 4. Learning Agents
Learning agents can become more competent over time.
They can start with an initially empty knowledge base.
They can operate in initially unknown environments.

81. There are a variety of designs
Reflex agents respond immediately to percepts.
Goal-based agents work towards goals.
Utility-based agents try to maximize their reward.
Learning agents improve their behavior over time.

82. Components of Learning Agents
learning element (responsible for making improvements)
performance element (has to select external actions) take percept and decide an action
critic (determines the performance of the agent) percept only doesn’t provide how much is the agent is successful
problem generator (suggests exploratory actions that will lead to new informative experiences)

83. Thank you End of Chapter 1