1. Artificial Intelligence
AI CS371
CS 362
April 21, 2017
Artificial Intelligence
(AI)
Lecture 1
Lecture 1,2 by dr. magdy saleh

2. Lecturers Contacts Anmar Abu Hamdah : anmar_81@hotmail.com
Dr. Abdul Ahad Siddiqi :
Dr. Magdy M Saleh :

3. Artificial Intelligence Artificial Intelligence through Prolog
AI CS371
CS 362
Artificial Intelligence
April 21, 2017
Text book
Artificial Intelligence Structures and Strategies for Complex Problem Solving Sixth Edition by
LUGER
Reference
Artificial Intelligence through Prolog By
Rowe, Neil C.
Lab .
Visual PROLOG
Lecture 1,2 by dr. magdy saleh

4. Artificial Intelligence CS362 Intelligent Systems
Course Information
Course
Number &
Code
Title
Credit
Units
Units Type
Study
level/year
Pre –requisite
(if any)
Co –requisite
Theory
Lab
CS 362
Intelligent Systems 3 -
6/3
CS 202
CS362 Intelligent Systems

5. Artificial Intelligence CS362 Intelligent Systems
Course Objectives
Develop an appreciation of the role of
intelligent systems in the contemporary
context.
2. Develop a deep understanding of fundamental
theoretical and practical concepts about
intelligent systems.
3. Develop several applications employing different
intelligent system paradigms
CS362 Intelligent Systems

6. Course Knowledge Outcomes
CS 362
Artificial Intelligence
Course Knowledge Outcomes
Examine the ideas and techniques underlying
the design of intelligent systems.
2. Learn various search techniques and how to
apply these in the design of intelligent systems.
3. Learn various knowledge representation techniques, and how to retrieve the knowledge using knowledge-based reasoning methods.
4. Investigate the computer understanding of sensory, and language information.
CS362 Intelligent Systems

7. Course Knowledge Outcomes (contd.)
CS 362
Artificial Intelligence
Course Knowledge Outcomes (contd.)
5. Identify examples of classification tasks, including the available input features and output to be predicted using Neural Network models.
6. Learn uncertainty and how to deal with incomplete knowledge using Probabilistic and Bayesian models.
7. Learn and understand the underlying concepts of intelligent systems, machine learning, natural language processing, and robotics.
8. Learn and use a pure functional programming language to develop a simple knowledge-based application.
CS362 Intelligent Systems

8. Artificial Intelligence CS362 Intelligent Systems
Course Grading
Grading
Student Evaluation
Midterm 1 Exam
20%
Midterm 2 Exam
Homework
10%
Group Project
Final Exam
40%
Total
100%
CS362 Intelligent Systems

9. CS362 Intelligent Systems
Course Schedule
Order
Topics List
Week Due
(contact hours) 1
Fundamental Issues 3 2
Predicate calculus 6
Search & Search Techniques 4
Heuristic search 5
Review & First Midterm Examination
Stochastic search & Probabilistic reasoning 8
4.5 7
Knowledge representation 9
Planning & Project Hand Out 10
Automated reasoning 11
1.5
Review & Second Midterm Examination
Neural networks 12
Machine learning 13
Robotics & Project Hand In 14
Understanding Natural Language Processing 15
 Review
15 
 1.5
CS362 Intelligent Systems

10. CS 362 Introduction to Artificial Intelligence Lecture 1: Overview

11. Overview of Artificial Intelligence (Definition)
The branch of computer science concerned with making computers behave like humans.
Computers with the ability to mimic or duplicate the functions of the human brain
The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology.

12. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Turing Test
The Turing test is a test of machine’s ability to exhibit an intelligent behaviour.
Fig 1.1 The Turing test.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

13. Artificial Intelligence Systems
The people, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that demonstrate the characteristics of intelligence

14. Artificial Intelligence (Behavior)
Intelligent behavior
Learn from experience
Apply knowledge acquired from experience
Handle complex situations
Solve problems when important information is missing
Determine what is important
React quickly and correctly to a new situation
Understand visual images
Process and manipulate symbols
Be creative and imaginative
Use heuristics

15. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Important Features of Artificial Intelligence
The use of computers to do reasoning, pattern recognition, learning, or some other form of inference.
A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem-solving technique.
A concern with problem-solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems.
Reasoning about the significant qualitative features of a situation.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

16. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Important Features of Artificial
Intelligence (contd.)
4. An attempt to deal with issues of semantic meaning as well as syntactic form.
5. Answers that are neither exact nor optimal, but are in some sense “sufficient”. This is a result of the essential reliance on heuristic problem-solving methods in situations where optimal or exact results are either too expensive or not possible.
6. The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems.
7. The use of meta-level knowledge to effect more sophisticated control of problem-solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential are of research.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

17. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Overview of AI applications
The two most fundamental concerns of AI researchers are knowledge representation and search.
Search is a problem-solving technique that systematically explores a space of problem state
Like most sciences, AI is decomposed into a number of subdisciplines that, while sharing an essential approach to problem solving, have concerned themselves with different applications.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

18. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Important Research and
Application Areas
Game Playing
Automated Reasoning and Theorem Proving
Expert Systems
Natural Language Understanding and Semantic Modeling
Modeling Human Performance
Planning and Robotics
Languages and Environments for AI
Machine Learning
Alternative Representations: Neural Nets and Genetic Algorithms
AI and Philosophy
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

19. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Game playing
Most games are played using a well-defined set of rules: this makes it easy to generate the search space and frees the researcher from many of the ambiguities and complexities inherent in less structured problems.
State space search, the paradigm underlying most game-playing research, is presented in Chapters 3 and 4.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

20. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Automated reasoning and
theorem proving
Theorem-proving research was responsible for much of the early work in formalizing search algorithms and developing formal representation languages such as the predicate calculus (Chapter 2) and the logic programming language Prolog.
Most of the appeal of automated theorem proving lies in the rigor and generality of logic.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

21. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Expert system
One major insight gained from early work in problem solving was the importance of domain-specific knowledge.
Expert knowledge is a combination of a theoretical understanding of the problem and a collection of heuristic problem-solving rules that experience has shown to be effective in the domain.
Expert systems are constructed by obtaining this knowledge from a human expert and coding it into a form that a computer may apply to similar problems.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

22. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Natural language understanding
and semantics
One of the long-standing goals of artificial intelligence is the creation of programs that are capable of understanding and generating human language.
Understanding natural language involves much more than parsing sentences into their individual parts of speech and looking those words up in a dictionary.
Real understanding depends on extensive background knowledge about the domain of discourse and the idioms used in that domain as well as an ability to apply general contextual knowledge to resolve the omissions and ambiguities that are a normal part of human speech.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

23. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Modeling human performance
The design of systems that explicitly model aspects of human performance is a fertile area of research in both AI and psychology.
Human performance modeling, in addition to providing AI with much of its basic methodology, has proved to be a powerful tool for formulating and testing theories of human cognition.
The problem-solving methodologies developed by computer scientists have given psychologists a new metaphor for exploring the human mind.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

24. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Planning and robotics
Research in planning began as an effort to design robots that could perform their tasks with some degree of flexibility and responsiveness to the outside world.
Briefly, planning assumes a robot that is capable of performing certain atomic actions. It attempts to find a sequence of those actions that will accomplish some higher-level task, such as moving across an obstacle-filled room.
Planning is a difficult problem for a number of reasons, not the least of which is the size of the space of possible sequences of moves. Even an extremely simple robot is capable of generating a vast number of potential move sequences
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

25. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Languages and environments of AI
Programming environments include knowledge-structuring techniques such as object-oriented programming. High-level languages, such as Lisp and Prolog, which support modular development, help manage program size and complexity. Trace packages allow a programmer to reconstruct the execution of a complex algorithm and make it possible to unravel the complexities of heuristic search.
Without such tools and techniques, it is doubtful that many significant AI systems could have been built.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

26. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Machine learning
Learning has remained a challenging area for AI. The importance of learning, however, is beyond question, particularly as this ability is one of the most important components of intelligent behavior. An expert system may perform extensive and costly computations to solve a problem.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

27. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
Neural Nets and Genetic Algorithms
Computer system that can act like or simulate the functioning of the human brain
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

28. CS362 Intelligent Systems Chapter 1 : Fundamental Issues
AI and Philosophy
The questions that Turing posed about intelligent programs reflect back on our understanding of intelligence itself. What is intelligence, and how is it described? What is the nature of knowledge? Can knowledge be represented? How does knowledge in an application area relate to problem-solving skill in that domain?
Answers proposed to these questions make up an important part of what AI researchers and designers do. In the scientific sense, AI programs can be viewed as experiments. A design is made concrete in a program and the program is run as an experiment. The program designers observe the results and then redesign and rerun the experiment. In this manner we can determine whether our representations and algorithms are sufficient models of intelligent behavior.
CS362 Intelligent Systems
Chapter 1 : Fundamental Issues

29. Artificial Intelligence
AI CS371
CS 362
April 21, 2017
Artificial Intelligence
(AI)
Lecture 2
Dr. Anmar Abuhamdah
Lecture 1,2 by dr. magdy saleh

30. Main subareas of artificial intelligence
Inference
Planning
Expert Systems
Neural networks
Data mining
Natural language
Computer vision
Robotics
Machine learning
Cognitive science
Distributed and social AI

31. Major Branches of AI (1) Perceptive system Vision system Robotics
A system that approximates the way a human sees, hears, and feels objects
Vision system
Capture, store, and manipulate visual images and pictures
Robotics
Mechanical and computer devices that perform tedious tasks with high precision
Expert system
Stores knowledge and makes inferences

32. Major Branches of AI (2) Learning system Natural language processing
Computer changes how it functions or reacts to situations based on feedback
Natural language processing
Computers understand and react to statements and commands made in a “natural” language, such as English
Neural network
Computer system that can act like or simulate the functioning of the human brain

33. Areas of Artificial intelligence
AI CS371
April 21, 2017
Areas of Artificial intelligence
Robotics
Vision systems
Learning systems
Natural language processing
Neural networks
Expert systems
Lecture 1,2 by dr. magdy saleh

34. Areas of Artificial Intelligence
AI CS371
April 21, 2017
Areas of Artificial Intelligence
Lecture 1,2 by dr. magdy saleh

35. Artificial intelligence = Software that acts intelligently

36. Artificial intelligence = Software that acts intelligently
AI (artificial intelligence) centers on methods using booleans, conditionals, and logical reasoning, with numbers used as needed.
AI software need not work like people do, but people can provide clues as to methods. Aircraft don't fly by imitating birds.
AI means deep (not superficial) understanding of how to do something (e.g. language understanding versus table lookup). Example: Query "picture of west wing of white house" for Google.

37. Artificial intelligence, continued
AI will become increasingly common in the future, as computers do increasingly complex tasks -- but people will increasingly forget that it is artificial intelligence.
Many developments in AI have been "exported" to other areas of computer science (e.g. object-oriented programming and data mining).
AI programs that are too slow today may get used eventually as computers increase in speed (e.g. speech understanding).

38. What is AI good for?
AI is not precisely defined, but generally it’s for:
Problems needing "common sense", like recognizing building types in aerial photos
Problems requiring many different kinds of knowledge, like automatic translation of English text
Problems only a few experts can solve, like treating rare diseases
Hard problems without any good known algorithms, like mission planning for a large military operation, or playing chess

39. History of Artificial Intelligence
AI CS371
April 21, 2017
Lecture 2
History of Artificial Intelligence
Lecture 1,2 by dr. magdy saleh

40. Some history of artificial intelligence
AI CS371
April 21, 2017
Some history of artificial intelligence
1950s: The first programs
first speculation about AI
game-playing
heuristic search methods
1960s: Major progress
Lisp programming language
Development of symbolic reasoning methods using logical constraints
The first natural language and vision programs
Lecture 1,2 by dr. magdy saleh

41. Some history of artificial intelligence
1970s: Many successes
Developments on many fronts
Appearance of expert systems
Prolog programming language and various other AI software
Symbolic learning is popular
1980s: Faddishness
Suddenly AI is faddish and gets much media coverage
Lots of AI startup companies, most fail
Lots of standalone AI applications, lots of expert systems
Neural networks become popular

42. Some history of artificial intelligence
1990s: Maturity of AI
AI no longer a fad, but used more than ever (e.g. the Web)
AI is embedded in larger systems (like on the Web and in simulations)
Genetic algorithms and artificial life are popular
Statistical language processing is popular, including speech understanding
2000s: AI is back in fashion
Data mining is popular
Simulations and games using AI are popular

43. Artificial Intelligence Today

44. Artificial intelligence today (1)
Programs that use artificial-intelligence techniques are usually just pieces of a larger system (like Java classes).
"Artificial-intelligence techniques" emphasize conditional statements and logical constructs (like "and", "or", and "not").

45. Artificial intelligence today (2)
Artificial-intelligence code often contains many small pieces, to provide flexible reasoning.
Journals with current AI trends: IEEE Intelligent Systems and AI Magazine. More technical are IEEE Transactions on Knowledge and Data Engineering and Artificial Intelligence.

46. Example AI applications today (1)
AI CS371
April 21, 2017
Example AI applications today (1)
Tax preparation software
Detection of credit-card fraud
Intrusion detection software for firewall machines
M1 tank engine diagnosis
Automated assistance to medics in remote sites
Tailored automobile repair instructions at dealers
Lecture 1,2 by dr. magdy saleh

47. Example AI applications today (2)
Summarizing terrorism reports from a newswire
Summarizing routine managerial data
Summarizing sensor data during warfare
Content-based Web search engines
Automatically generated indexes to permit finding similar chemicals

48. Example AI applications today (3)
Home-vacuuming robot
Guidance for autonomous vehicles
Automated intelligent pilots for combat simulation
Scheduling of aerial medical evacuation
Automatic distributed meeting scheduling
Planning for customized product configurations like for computers

49. Example AI applications today (4)
Detecting changes to buildings in aerial photographs
Automatic translation of European government documents
Inference of dangers in new chemical products from similar cases

50. Options for implementing artificial intelligence:
Use an AI programming language
Use an AI software package
Do AI directly in your favorite programming language

51. 1- Use an AI programming language
Prolog and Lisp are the two programming languages; both have standards.
Both emphasize programming in small pieces.
Both use emphasize linked lists and recursion.
Lisp uses functions, Prolog predicate calculus.
Prolog has automatic backtracking and flexible variable binding.

52. 2. Use an AI software package
Many are available -- CLIPS is a popular standalone system, JESS is a popular Java package.
Packages have better support facilities (e.g. graphics) than languages
Languages have standards, shells don't.
Packages may be too rigid for your application.
Some packages don't support variables (e.g. neural nets).

53. 3. Do AI directly in your favorite programming language
This is the most popular way today.
Libraries and predefined classes for AI methods are available.
It's more work to do it yourself, but many AI ideas aren't hard to implement.

54. Key difficulties in doing AI
Successes get exported, and people forget the ideas came from AI.
Thorough testing is necessary to show an AI system works. Don't trust quick demos.
Methods that cannot be tested easily (e.g. genetic algorithms and fuzzy sets) tend to be overvalued because people cannot see when they're wrong. Methods that can show obvious errors (e.g. logical inferences) tend to be undervalued.

55. End of Chapter 1 AI CS371 April 21, 2017
Lecture 1,2 by dr. magdy saleh