CSC 550: Introduction to Artificial Intelligence
Spring 2004
Text:
|
Artificial Intelligence: Structures and Strategies for Complex Problem
Solving (4th ed.), George F. Luger, Addison-Wesley, 2002.
|
Course Description
Artificial Intelligence is the subfield of computer science concerned with
automating tasks that would require "intelligence" if performed by people.
AI is a highly eclectic field, with roots in mathematics, logic, psychology,
philosophy, and engineering. The goal of this course is to introduce and survey
the field of Artificial Intelligence, paying special attention to foundational
concepts and theories. In addition, current trends and approaches in AI
research will be studied.
Specific goals for the course are:
- To survey the field of Artificial Intelligence, including major areas of
study and research (e.g., reasoning, planning, understanding)
- To study the foundational concepts and theories that underlie AI, including
search, knowledge representation, and sub-symbolic models.
- To contrast the main approaches to AI: symbolic vs. emergent.
- To provide practical experience developing AI systems using the functional
programming language Scheme.
Required Work
There will be five to seven homework assignments spread throughout the term.
These assignments will cover concepts and problems from class and the readings,
and may involve writing and modifying AI programs in Scheme. Assignments are due
at the beginning of class on the date specified. Late assignments will
receive 75% of full credit if they are handed in within one week of the
specified due date. After one week, no credit will be given. In addition,
students will be expected to independently research a topic in AI and
present that topic to the class. There will be weekly quizzes, a midterm exam
and a cumulative final exam (see the schedule below for exam dates).
There is no specific attendance policy for the course, although it is expected
that absences will leave the student unprepared for tests and assignments. Quizzes
and tests will not be rescheduled except in extreme circumstances. However, the
lowest quiz grade will be dropped.
Grades will be determined as follows:
homework assignments |
35 % |
student presentation |
10 % |
weekly quizzes |
05 % |
midterm exam |
20 % |
(cumulative) final exam |
30 % |
At the minimum, traditional grading cutoffs will apply. That is,
90% is guaranteed an A, 87% is guaranteed a B+, etc.
Depending on class performance, some shifting of grades (in an upward direction only)
may occur as final letter grades are assigned.
Policy on Collaboration
The college policy on cheating and plagiarism is spelled out in the Student
Handbook. In addition to this, the following guidelines hold pertaining to
programs. Programs are to be the sole work of the student -- collaboration on
the design or coding of a program is not allowed. Students may seek debugging
assistance or clarifications on assignments using the class mailing list:
csc550@creighton.edu.
Repeat: All student interactions regarding homework assignments must
take place via the class mailing list!
Tentative Schedule
DATES |
TOPICS |
READINGS |
HOMEWORK |
Jan 20 |
| Chapter 1 |
27 |
| Chapter 15, Online |
HW1: due 2/10
|
Feb 3 |
| Chapter 2 |
10 |
AI AS SEARCH
state spaces, uninformed strategies. |
(ppt)
(pdf) |
| Chapter 3 |
|
17 |
| Chapters 4, 5 |
HW2: due 3/2 | -->
24 |
Search applications,
Algorithm A, admissibility, |
(ppt)
(pdf) |
| |
Mar 2 |
| |
Presentation |
9 |
SPRING BREAK |
16 |
game trees, minimax,
alpha-beta pruning. |
| |
HW3: due 3/30 |
23 |
REPRESENTATION & AI semantic nets,
frames. |
(ppt)
(pdf) |
| Chapter 6 |
|
30 |
Expert systems, uncertainty rule-based
reasoning. |
(ppt)
(pdf) |
| Chapters 7, 8 |
HW4: due 4/6 |
Apr 6 |
MACHINE LEARNING connectionist
models,
|
(ppt)
(pdf) |
| Chapters 9, 10 |
|
13 |
| HW5: due 4/27 |
20 |
| Chapter 11 |
27 |
|
May 4 |
FINAL EXAM (Tue 5:00 - 7:45) |