CIS 630
Survey of Artificial Intelligence I: Basic Techniques

Description

A survey of the basic concepts and techniques of Artificial Intelligence including Problem Solving, Knowledge Representation, Planning, Learning, and Natural Language Processing.

Objectives

Upon completion of this course, you should be able to:

• Turn a problem into a representation suitable for a computer,
• Choose an appropriate technique for solving this problem computationally,
• Understand the AI terminology.

Instructor

Nathalie Japkowicz, Office: DL 250, Office Hours: Tuesdays 11-1

TA

Huaxing Wu, Office DL 474, Office Hours Tuesdays-Thursdays 1:30-2:30

Meeting Times

Tuesdays and Thursdays, 9:30-10:45, Dreese Labs, 280

Prerequisites

• Programming, Algorithms, and Data Structures (CIS222),
• Discrete Mathematics (Math366),
• or Permission of the instructor.

Text

• Dean, T., Allen, J. and Aloimonos, Y.: Artificial Intelligence, Theory and Practice , The Benjamin/Cummings Publishing Compagny, Inc., 1995.

Grading:

• Assignments/Labs-----40 %
• Midterm-------------30 %
• Final---------------30 %
• Total:--------------100 %

Homeworks and Exams:

Homework assignments are due at the beginning of a class period. There will be no make-up on homeworks. Four homeworks are scheduled for the class at the following dates:

• Assignment #1-----Due: January 26, 1999
• Assignment #2-----Due: February 9, 1999
• Assignment #3-----Due: February 23, 1999
• Assignment #4-----Due: March 9, 1999

• Midterm----------February 9, 1999
• Final: Consult Master Schedule for final exam date and time

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Schedule (Tentative):

(You are responsible for keeping up with schedule changes announced in class)



Lecture Schedule:

Week	Topic	Readings
1	What is Artificial Intelligence?	Chapter 1
Problem Representation	Section 4.1
2	Blind Search	Sections 4.2
Heuristic Search	Section 4.3
Assignment # 1: Problem Solving and Search	Due: January 26
3	Optimization and Search	Section 4.4
Adversarial Search	Section 4.5
4	Adversarial Search (Cont'd)	Section 4.5
Propositional Logic	Sections 3.1 and 3.2
Assignment # 2: Adversarial Search and Knowledge Representation	Due: February 9
5	Predicate Calculus	Section 3.4
Midterm Review
6	Midterm
Resolution Proof	Sections 3.5 and 3.6
Assignment # 3: Decision Tree Learning	Due: February 23
7	Resolution Proof (Cont'd)	Sections 3.5 and 3.6
Deductive Retrieval Systems	Section 3.8
8	Learning Overview, Theory, and Version Spaces	Sections 5.1,5.2, 5.3
Decision Trees and Neural Networks	Sections 5.4 and 5.5
Assignment # 4: Natural Language Processing	Due: March 9
9	Natural Language Processing: Syntax	Sections 10.1, 10.2, 10.3
Natural Language Processing: Semantics and Pragmatics	Sections 10.4 and 10.5
10	Planning	Sections 7.1,7.2, 7.3
Final Review
Final Exam	Check Master Schedule