Artificial Intelligence (CS435)



Course Portfolio

Faculty of Science

Computer Science Department

COURSE NAME: Artificial Intelligence

COURSE NUMBER: CS435

SEMESTER/YEAR: -------------------------------------------

DATE: -------------------------------------------------------------

Instructor Information

← Dr. Gibrael Al Amin Abo Samra.

← Faculty of Science; Main Building 115, Room 512.

← Contact number(s): ext. 64241.

← E-mail address: gabosamra@

← Welcome to the Artificial Intelligence (AI) Course. You will enjoy understanding what AI is, when we need to apply AI techniques and how some of these techniques are implemented. You will also enjoy understanding the basics of expert systems. Finally you will have some practice on one of the most familiar AI programming Languages (PROLOG).

PART II

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COURSE SYLLABUS

Course Information

Course Name: Artificial Intelligence 

Course Code: CS435 

Course meeting times: Sunday and Tuesday at 11:00 to 12:30

place: building40 room 210

Prerequisite: CS221

Artificial Intelligence(AI) def:- AI is the branch of science that tries to automate the intelligent behavior of the human to allow computers to perceive, reason, and decide.

Course Objectives

Course Objectives

This course introduces students to basic concepts and methods of artificial intelligence from a computer science perspective. Emphasis of the course will be on the selection of data representation and algorithms useful in the design and implementation of intelligent systems. The course will contain an overview of one Al language and some discussion of important applications of artificial intelligence methodology.

 At the end of this course the students will be able to :

1. Select a knowledge representation scheme suitable for a real life problem.

2. 2-Apply a suitable search algorithm to get a solution depending on the problem goal.

3. 3-Use an AI programming language to implement simple expert systems.

 

Benefits of this Course

Students in this course will get the skills and the required background that enable them to build Intelligence systems in different application areas.

Learning Resources

 

Required Textbook:

• Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger, Addison Wesley, 2002.

• Artificial Intelligence (Third Edition) by Patrick Henry Winston, Addison ~ Wesley, 1992.

• Notes written by Prof. M. Ghonaim.

• Slides for certain topics

Software Needed:

PROLOG version 6.X: available at the computer lab.

Course Requirements and Grading

 Evaluation:

30% Project, Homework, and lab.

1. 10%: Home work: Solution of the exercises at the end of each chapter.

All students should solve the problems themselves to be able to solve problems in the exams. If copy of solution is detected the 10 marks are lost.

2. 10%: Project: Apply one of the AI techniques on a real problem or a game and introduce this work written on paper and /or stored on a floppy or compact disk.

A maximum of three students are allowed to join in one project

Projects shouldn't be repeated, if it happens, the time of submission is taken into consideration.

Three parameters are considered in the evaluation of a project:

• The originality of the project's idea.

• The understanding of the used techniques.

• The level of implementation of the project.

3. 10: Implementation & Trace of a PROLOG program which contains facts, rules, and goals.

30% First and Midterm exams

1. 10%: First exam covers the Search Techniques.

2. 20%:Mid-term exam covers Knowledge Representation and Expert Systems.

40% Final Exam

Covers all the topics of the course.

Course Outline:

 

• Introduction

o What is Artificial Intelligence?

o Is Al Possible?

o Some Al Tasks.

 

• Using Search in Problem Solving

o Introduction

o Basic Search Techniques For Trees

• Simple Search Techniques

o Depth first Search

o Breadth first Search

• Heuristic Search Techniques

o Hill Climbing

o Beam Search

o Best First Search

• Optimal Search Techniques

o Branch and Bound Search

o Branch and Bound Search Augmented by Underestimation

o Branch and Bound Search with Eliminating Redundant Paths

o The A* Algorithm

• State Space Search Algorithms

o Breadth First Algorithm

o Depth First Algorithm

o Best First Algorithm

• Knowledge Representation and Inference

o Introduction

o Logical Representation Schemes

▪ Prepositional Calculus

▪ Predicate Logic

▪ Review of Prepositional Logic

▪ Predicate Logic: Syntax

▪ Predicate Logic: Semantics

▪ Proving Things in Predicate Logic

▪ Representing Things in Predicate Logic

▪ Network Representation Schemes

▪ Semantic Networks

▪ Conceptual Graph

▪ Structured Representation Schemes

▪ Frames

• Expert Systems

o Introduction

o Designing an Expert System

o Expert System Architecture

o Choosing a Problem

o Knowledge Engineering

o Rules and Expert Systems

▪ A Simple Example

▪ Explanation facilities

▪ More Complex Systems

▪ Rule-Based Systems

▪ Forward Chaining Systems

▪ Backward Chaining Systems

▪ Forwards vs. Backwards Reasoning

o An Expert System Shells<

o MYCIN: A Quick Case Study

• Artificial Intelligence Programming in Prolog

o Artificial Intelligence Programming

o The Main Al Languages

o The Basics of Prolog

▪ Prolog Terms, Backtracking and Unification

▪ Basic Data Structures and Syntax

▪ More about Prolog Matching

▪ Backtracking

▪ Declarative and Procedural Views of Programs

▪ Some Exercises

▪ Recursion

▪ Tracing Prolog Execution

▪ Exercises

|Course Schedule Model |

|(meeting two times a week) |

|Week # |Date |Topic |Reading Assignment |What is Due? |

|1 | |Introduction to the course |Chapter 1 |Buy Book |

| | |AI Applications areas | | |

|2 | |Blind Search | |Problem set 1( 1,2,3,4) |

| | |Heuristic Search | |Problem se 1(5(a, b)) |

|3 | |Optimal Search | |Problem set 1(5(c, d)) |

| | |A* Algorithm | | |

|4 | |State Space Search Algorithms | |Problem set 1 (6-10) |

| | |Revision of Search techniques | |Project #1 |

|5 | |First Exam | | |

| | |Knowledge Representation and Inference (introduction) | | |

| | |Introduction | | |

|6 | |Prepositional Calculus | |Problem set 2(1,2) |

| | |Predicate Logic: Syntax | | |

|7 | |Predicate Logic inference rules | |Problem set 2(4) |

| | |Semantic Networks | |Problem set 2(3) |

|8 | |Conceptual Graphs | |Problem set 2(5) |

| | |Structured representation: Frames | |Problem set 2(6) |

|9 | |Expert Systems: Introduction | | |

| | |Rule-Based Systems | | |

|10 | |Forward Chaining Systems | |Problem set 3( 1-a) |

| | |Backward Chaining Systems | |Problem set 3( 1-b) |

|11 | |Forwards vs Backwards Reasoning | | |

| | |An Expert System Shell | | |

|12 | |Reaction based systems | | |

| | |Mid Term Exam | | |

|13 | |Artificial Intelligence Programming | | |

| | |PROLOG syntax: | | |

| | |PROLOG databases and quires | |Program1:family relations |

| | |Rules in PROLOG | |Deduce relations using rules |

|15 | |Backtracking in PROLOG | |Trace program1 |

| | |Lists and recursion in PROLOG | | |

| | |Final Exam all sections | | |

PART III

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COURSE RELATED MATERIAL

Contains all the materials considered essential to teaching the course, includes:

lab quizzes, mid-terms, and final exams and their solution set

Paper or transparency copies of lecture notes/ handouts (optional)

Practical Session Manual (if one exists)

Handouts for project/term paper assignments

PART IV

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EXAMPLES OF STUDENT LEARNING

Examples of student work. (Included good, average, and poor examples)

Graded work, i.e. exams, homework, quizzes

Students' papers, essays, and other creative work

Final grade roster and grade distribution

PART V

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INSTRUCTOR REFLECTION (optional)

Part V. Instructor Reflections on the Course

There is a big need for a projector to show real examples and to illustrate huge problems.

There is a big need for an assistant person to allow for more projects and support problem solving.

There is a big need for lab hours to experiment PROLOG examples.

There is a big need for a website for the course with editing tools to allow for improvements.

Course Portfolio

chECKLIST

[pic]TITLE PAGE

[pic]COURSE SYLLABUS

[pic]COURSE RELATED MATERIAL

[pic]EXAMPLES OF EXTENT OF STUDENT LEARNING

[pic]INSTRUCTOR REFLECTION ON THE COURSE

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