This course is an introductory course to artificial intelligence. The purpose of this course is to provide an overview of this field. We will focus on problems in the field of AI and techniques and algorithms for  solving  those  problems,  therefore  we  will  cover  topics including:  agents,  search,  planning, Uncertainty and learning. Students will not be expected to have any prior knowledge of AI, but they will  be  expected  to  have  good programming  skills  and  a  grasp  of  basic  theoretical  techniques  for analyzing computer algorithms.

Course code: CS 208

Course name: Introduction to Artificial Intelligence

Course Description

This course is an introductory course to artificial intelligence. The purpose of this course is to provide an overview of this field. We will focus on problems in the field of AI and techniques and algorithms for  solving  those  problems,  therefore  we  will  cover  topics  including:  agents,  search,  planning, Uncertainty and learning. Students will not be expected to have any prior knowledge of AI, but they  will  be  expected  to  have  good  programming  skills  and  a  grasp  of  basic  theoretical  techniques  for analyzing computer algorithms.

Course Objective

By the completion of the course the students should be able to

·        To have  an  appreciation  for  and understanding  of both  the  achievements  of AI  and  the  theory underlying those achievements.

·        To have  a  basic proficiency  in  a  traditional AI  language  including  an  ability  to write  simple  to intermediate programs and an ability to understand code written in that language. 

·        To have an understanding of the basic issues of knowledge representation and blind and heuristic search, as well as an understanding of other topics such as minimax, resolution, etc. that play an important role in AI programs.

·        To have an understanding of the Logics, Propositional log, First Order Logic.

·        Have a good knowledge of Prolog language in addition to use Java for AI applications.

References

Required:

1-    Artificial  Intelligence: A Modern Approach, Third Edition, Stuart Russell & Peter Norvig , Pearson Education Inc., 2010 - ISBN: 978-0-13-604259-4.

Recommended:

1-    Introduction to Machine Learning, Ethem Alpaydin, MIT Press, 2010 ISBN: 978-0-262-01243-0

Prerequisite

Discrete Mathematics

or Digital logic design

Evaluation Method:

Method	Percentage
Quizzes & Assignments	10%
Lab and Projects	20%
Test 1 & Test 2	30%
Final Examination	40%

Teaching plan:

W	Topic Name	Sub Topic	Reading Chapter
1	Introduction	AI scope, goals, and policies of the course.   Fundamentals of Artificial Intelligence (AI),  History and AI disciplines.	Chapter 1
2	Intelligent Agent	Agent  types  and  Environments   The concept  of  rationality,   the  nature  of  environments,  structure of agents. Homework 1	Chapter 2
3	Problem solving I	Solving Problem by Searching (part 1( Problem Solving and Search:  Problem solving agents, example problem,  and problem formulation using  State Space Representations.  Searching for solutions, uniformed search strategies – Breadth first search,   depth first search,  Depth limited search,  Iterative-deepening depth first search  bi-direction search  comparison.	Chapter 3
4	Problem solving II	Solving Problem by Searching (part 2( Informed (Heuristic) search strategies: Search with partial information (Heuristic search) Greedy best first search,  A* search,  Memory bounded heuristic search,  Heuristic function.	Chapter 3
5	Beyond Classical Search	Heuristic  Functions,  optimality,   Local Search and Optimization  Local  search  Algorithms:   Hill climbing,  simulated annealing search,  local beam search,  genetic algorithm. Homework 2	Chapter 4
6	Adversarial Search	Games, Min-max algorithm,  optimal decisions in multiplayer games,  Alpha-Beta pruning,  Evaluation functions.  cutting of search.	Chapter 5
7	Knowledge, reasoning, and planning: Logical Agents	Knowledge Base Agents, Logic, propositional Logic and Inference: a very simple Logic.	Chapter 7
8		Review of all the above Mid – Term Exam.
9	First-Order Logic	Syntax and Semantics of FOL. Using FOL, Knowledge engineering in FOL.	Chapter 8
10	Learning from Examples	Forms of Learning, Supervised Learning, Learning Decision Tree, machine Learning basics. Regression and classification of with Linear Model, Artificial Neural Networks	Chapter 18
11	Natural Language Processing	Language Models, Text classifications.	Chapter 12
12	Prolog programming	Facts, Rules and lists. Programming with prolog.	Prolog exercise
13	Lab: Practical Prog.	Course Projects Overview.	Chapters 13,14&15
14	Course Project	Projects Presentations, Course Revisions.	Chapters 22&23
15		Presentation & Revision for Final Exam

The following table introduces the handouts to the students. Some of the materials are adopted from other sites with slight modification. All feedbacks for improvement are welcomed. 

Course contents:     

Week		Subject	Handouts
1	Introduction I	AI Definitions	Lecture1
2	Introduction II	AI History and applications	Sheet1
3		National Day
4	Agent	Agent Terminology	Lecture2
5	Agent programs	Agent types	Sheet2
6	Midterm Outlines	Review for the mid term1	Solution for sheet1 Solution for sheet2
7		Midterm 1
8	Prolog programming I	Facts, Rules and lists, Horn clues and  Programming with prolog.	Lecture3
9	Prolog programming II	Resolution, Unification and backtracking.	Prolog Exercises
10	Problem solving I	uniformed search strategies Breadth first search,  depth first search,  Depth limited search,  Iterative-deepening depth  first search  bi-direction search   comparison.	Lecture4A Lecture4B
11	Problem solving II	Informed (Heuristic) search: Search with partial information (Heuristic search)  Greedy best first search,  A* search,  Memory bounded heuristic search,  Heuristic function.	Lecture5
12	Knowledge representation	Different approaches  to represent Knowledge	Lecture6
13	Knowledge, reasoning, and planning	Logical  Knowledge  representation	Lecture7
14		Midterm 2
15	Final Examination