Download 601451 AI-Syllabus File

Petra University
(Private Accredited University)
( )
‫آ ات‬
‫ اب‬
Faculty of Information Technology
Department of Computer Science
Course Title
Course No.
Credit Hrs
Artificial Intelligence
601452
3
Instructor Name
Office
e-mail/Web Site
Prerequisite
601351 –
Algorithms
Design &
Analysis
Year (semester)
Lec./Lab. Credit
2008-2009(3)
Lecture: 3
Lab : 0
Office Hours
Coordinator
Dr. Ghassan Issa
Text Book
Artificial Intelligence Structures and Strategies for Complex Problem Solving
George F Luger & W. A. Stubblefield, 5th ed., Addison-Wesley, 2005
Course Description
This course presents an introduction to the essential concepts and techniques of AI
and its applications’ areas. The course focuses on the major sub-disciplines of AI
such as: problem spaces, search strategies, knowledge representation, logic and
inference mechanisms, automated reasoning and problem solving techniques.
Other topics are introduced include Expert Systems, Intelligent Agents, Machine
Learning, and Natural Language processing.
Aims
The main goal of this course is to give the students a practical and a theoretical overview of the AI technology and
its fields through acquiring Conceptual Knowledge about Complex Problem Solving techniques and programming
in logic. For achieving this goal there are intermediate objectives, which have to be reached during the different
phases of the course. These sub objectives can be summarized as follows:
Objectives –
AI Fundamental issues & Applications: The difference between AI Approaches and Traditional Approaches,
Features of AI, Turing Test, principles of Artificial Intelligence, Heuristics, Introduction to Knowledge
Representation and problem solving, introduction to AI Application areas.
Search and Constraint Satisfaction: State Space representation and its components, Uninformed search,
Combinatorial explosion, Heuristic search, Characteristics of heuristic function, finding the optimal solution,
Game Search, utilizing backtracking for Solving a Constraint satisfaction Problem
Knowledge Representation and reasoning: represent knowledge using logical Formulas and prepositional
logic, how to utilize resolution and unification as theorem proving method, shortcoming of two valued logic,
basic principles of model theory, Search, Backtracking in PROLOG and Backward Chaining and Theorem
Proving in PROLOG
Advanced knowledge and Representation Reasoning under Uncertainty: how express uncertain knowledge
using probability theory, Bayes Theorem, shortcoming of probability theory based knowledge representation
and reasoning, Fuzzy Logic for representation of Imprecise and/or Uncertain knowledge.
AI Planning Systems: the basic concepts of a planning system, the distinction between problem solving as a
search problem and planning system, planning in robotics.
1
Natural Language Processing: role of knowledge Representation in a Natural Language Understanding
Systems, syntax checkers based on different type of grammars, use PROLOG as a tool for programming a
NLP system based on examples for Arabic.
Intelligent Agents: architecture of an agent, agents that are capable of keeping track with their environments,
the applications of the agent approach.
Non-Symbolic AI and Emergent Fields: Neural Nets, Genetic Algorithms, Artificial Life
Machine Learning: SYMBOL-BASED Vs. CONNECTIONIST, Version Space, The ID3 Decision Tree
Induction Algorithm, Inductive Bias and Learnability
Intended Learning Outcomes :
Successful completion of this course should lead to the following learning outcomes :
A- Knowledge and Understanding (students should):
A1. Identify the different Application areas of AI
A2. Understand the concept of problem solving as search, and learn how to use the various and Heuristic Search
Techniques.
A3. Understand the concepts and methodologies of Knowledge Representation.
A4. Understand the basics of AI areas including Expert Systems, Case-Based and Model-Based Reasoning, Natural
Language Processing, Neural Networks, Intelligent Agents, and Machine Learning.
B- Intellectual Skills (Student should be able to):
B1. Analyze, compare and criticize the different search techniques
B2. Synthesize modified search algorithms from existing ones.
B3. Contrast the main approaches to AI: symbolic((logic, semantic nets, rules) vs. emergent (connectionist,
genetic)
B4. Analyze and compare inference mechanisms.
C) Subject Specific Skills (Student should be able to):
C1. Solve a problem requiring a suitable knowledge representation and a search method
C2. Learn the essentials of the Prolog Programming Language and experiment with an expert system shell
C3. Write a report on a selected AI area
D) Transferable Skills (Student should be able to):
D1. work in a group in order to design and implement solutions of several AI problems.
D2. Conduct research and Present results
D3. Deploy communication skills
D4. Deploy proper report writing skills
Teaching and Learning Methods:
Interactive lectures (IlOs: A1, A2, A3, A4)
Lecture on major concepts and issues: Interactive lectures with videos and PowerPoint slides are
conducted with lecturer explaining and illustrating the concepts. Students will be invited to share
their view and experience in applying the concepts.
Group Projects and Presentations (ILOs: B1, B2, B3, C1, C2, C3, D1, D2, D3, D4)
Students will work on a course projects (2 to 3 students in a group). Each group will submit a short proposal of
their project, including the names of team members starting from the second week of classes. Once the project is
approved by the instructor, the group submits a more extended proposal which includes the role of each team
member, Time-Plan, and the tools and applications that will be employed in the project. Each group will submit
their project with a presentation at the end of the semester.
2
Online search / research and short presentations (ILOs: C2 , C3, D1, D3)
Each student will be required to search the net for a new topic that relates to this course. A one page summary of
this topic is to be submitted a long with a 10 minute presentation.
Textbook Problems (ILOs: A1, A2, A3)
Problems have been selected for in-class illustration of certain concepts and applications. Additional
textbook problems have been assigned for students to practice and gain better understanding of the
concepts discussed. Homework assignments will be collected for grading.
Outside-classroom activities (ILOs: B3, C1 ,D2, D4)
Students are required to schedule meetings with their groups, and to document the results of such meetings.
AI Lab (ILOs: C1, C2, C3)
Students are required to visit the AI lab and to experiment with Prolog and the expert System Shells available.
Course Contents :
Week
Topics
1
Introduction
2
AI Principles
3
Strategies for State Space
Search
4
Topic Details
What is AI? The foundations of AI; The history of AI;
Reference
(chapter)
Chp.1
Application Areas in AI
Features of AI work, Knowledge Representation, Search
and Problem Solving, Backward/Forward Search
Problem Space, Uniformed Search (Graph-Search, BruteForce Search, Depth-First, Breadth-First, Depth-first with
iterative deepening)
Chp.1, Ch3,
Notes
Chp.3,
Chp.4
Heuristic Search
Simple-Hill Climbing, Steepest-Ascent Hill Climbing
Heuristic Evaluation Function
Characteristics of Heuristic function
Chp.4
5
Heuristic Search
Chp.4
6
Heuristics in Games
Best-First Search
Admissibility, Monotonicity, Informdness
A* Algorithm
MinMax (Full-ply and two-Ply)
Alpha-Beta Procedure
7
Knowledge
Representation
Chp.4
First Exam
Logic as Basic for
Knowledge
Representation and
reasoning
8
Logic and PROLOG
9
Introduction to Agents
Types of knowledge. Methods of Knowledge
representations: Logic Representation
Frames. Object-Oriented Representation, Semantic
Networks
Logic and Programming in Logic
Prepositional logic
Predicate Logic first Order
Syntax:
Terms, Formulae, Literals, Quantifiers,
Clause Form, Horn Clauses
Semantics
Interpretation
Logical Consequence
Resolution Principle
Resolution Refutation Principle
Substitution
Unification
Resolution and Theorem Proving, Introduction to
PROLOG
Designing Intelligent Agent
3
Chp. 2, 7
Chp.7, 13
Chp. 7
Assessment
Agent
Human Agent
Software Agent
Robot Agent
Simple-Reflex-Agent
Intelligent Agent
Simple Planning agent, Planning as Search Problem
Representation of Planning problems, Block World Robot
Planner
Probabilistic
Conditional Probabilities
Bayes Theorem
Certainty Factors
Believe Functions
Fuzzy Logic: Compositional Rule of Inference
10
Basic concepts of
Planning
11
Reasoning under
Uncertainty
12
Rule-Based Expert
Systems
13
14
Non-Symbolic AI
15
Natural Language
Understanding
Notes
Chp. 8
Chp.5, 9
Second Exam
Overview of Expert System Technology;
Rule-Based Expert Systems
Model-Based, Case Based, and Hybrid Systems
Forward and Backward Chaining
Implementation issues
Neural Nets, Genetic Algorithms, Artificial Life
Chp. 8
Chp.11, 12
SYMBOL-BASED Vs. CONNECTIONIST,
Version Space, The ID3 Decision Tree Induction
Algorithm, Inductive Bias and Learnability
Role of Knowledge in Language Understanding,
Deconstructing Language: A Symbolic Analysis, Syntax
and Knowledge with ATN Parsers, Natural Language
Applications (Arabic NLP)
Machine Learning
16
Chp.10, 11
Chp.14
FINAL
EXAM
Final Exam
CONTINUAL COURSE Quality IMPROVEMENT
The following Measures are taken seriously to continuously improve the quality of the course:
Student Feed back: Using the University Student Evaluation, and the IT faculty Special Evaluation
Form to provide instructor and department with feedback.
Peer Visitation: Feedback from faculty members with similar specialization
Course Coordinator: Participates in course updates, and monitors course progress
Internal Examiner: Feedback pertaining to course outline, exams and projects, Course objectives and
ILOs
External Examiner: Feedback pertaining to course outline, exams and projects, Course objectives and
ILOs
ACM, AIS, and AITP Curriculum Guidelines
MOH Guidelines for Standard Efficiency Exams
Assessment and Grade Distribution
Assessment
I. Group Work
Project
Presentation
II. Individual Work
Attendance,
Participation, and
Home works
Quizzes
First Exam
ILOs
Requirement for Grading / Due Date
B1, B2, B3, C1, C2, C3, D1, D2, D3, D4
Proposal + written Report
85%
Discussions,
Unannounced Short quizzes
Covers Chapters 1, 3, 4
4
Total
15%
10%
5%
Power Point Slides
A1, A2, A3, B1, B2, B3, C1, C2, C3
Chapter Homework,
Presentations
Points
Short
5%
15%
Second Exam
A Comprehensive
Final examination
Fifteen Multiple Choice Questions worth
25% of exam Grade. Four to Five Essay
Questions worth 75% of exam grade.
Covers Chapters 2, ,5, 7, 8, 9, 13
Fifteen Multiple Choice Questions worth
25% of exam Grade. Four to Five Essay
Questions worth 75% of exam grade.
Covers Chapters 1–5, 7-14
25 Multiple Choice Questions worth 25% of
exam Grade. Five to six Essay Questions
worth 75% of exam grade.
TOTAL
15%
50%
100%
* Make-up exams will be offered for valid reasons. It may be different from regular exams in content and format.
References:
1. Artificial Intelligence A Modern Approach
Stuart Russel and Peter Norvig, Prentice Hall, 2nd ed. 2003
2. Artificial Intelligence A Guide to Intelligent Systems
M. Negnevitsky, Addison Wesley; 2 edition 2004
3. Artificial Intelligence: Theory and Practice
Thomas Dean, Addison Wesley; 1st edition (May 10, 2002)
4. Artificial Intelligence, 3th Edition)
Patrick Henry Winston, Addison Wesley; 3 edition (January 15, 1992)
5. PROLOG Programming Language For Artificial Intelligence
Ivan Bartko, Addison Wesley; 4 edition (13 Nov 2008)
6. Artificial Intelligence, Nils J. Nilsson
Morgan Kaufmann Publishers (1998)
7. Lecturer’s Notes
COURSE POLICIES
The University Regulations on academic dishonesty will be strictly enforced! Please check the University Statement
on plagiarism.
Make-up Exams: Only students with valid excuses are allwed to have make up exams. All excuses must be signed by
the Faculty Dean. Student has the responsibility to arrange with his/her instructor for an exam date before the
occurrence of the next regular exam.
All assignment and class work must be submitted at the specified due date. No late work will be accepted.
Attendance policy will be strictly enforced (refer to student's Handbook).
No make up for quizzes under any circumstance.
Last updated by Dr. Bassam Haddad 18. 02, 2008
Approved by:
Course Coordinator
Curriculum Committee
Quality Assurance Committee
Faculty Dean
Name
Date
5
Signature