UNIVERSITY OF DAYTON
CPS 480/580: Introduction to Artificial Intelligence
Spring 2011 3 credits T Th 1:30- 2:45pm
207 Miriam Hall
|
Professor: Dr. Jennifer Seitzer
Office: 144 Anderson Hall Email: seitzer@udayton.edu Course Web Page:
http://homepages.udayton.edu/~jseitzer1/cps480 Phone: (937)
229-2197 *Office Hours:
|
Mailing Address: Dr. Jennifer Seitzer, Associate Professor Computer Science Department University of Dayton 300 College Park Dayton, OH 45469-2160 |
Prerequisites:
CPS 350 Data Structures and Algorithms.
Catalog Description:
Basic concepts and techniques of intelligent systems. Emphasis on representations,
problem solving, search strategies, expert systems, logic systems, and AI
programming. Design
and implementation of AI applications.
Motivation:
Intelligence
is an elusive concept. Artificial
intelligence is more elusive because, here, we attempt to simulate this
unquantifiable and uncodifiable dictating force of
humans. We use many techniques to do
this, none of which is all-inclusive, but all together, continue to give us
more effective and smarter systems.
We will
study these techniques. Primarily, in
this semester, we will focus on traditional “good old-fashioned AI” (GOFAI) which
concentrates on search and knowledge representation. We will also introduce the concept of an agent:
any entity that perceives its environment through sensors and acts
upon or changes its environment through actuators or effectors. In this class, we will consider many
manifestations of agents. We will
approach the fundamental techniques of classical artificial intelligence,
knowledge representation and heuristic search strategies, using agents to
implement, understand, and elucidate. We will learn the basics of the
programming languages LISP, PROLOG, and SPARQL and examine how knowledge
representation on the Semantic Web is represented. We will finalize our study by considering
embodied intelligent agents in the form of robots.
Objectives:
Subject
Matter (Tentative list and schedule of
coverage):
|
Week |
Date |
Topics |
|
1 |
1- Tues, 1/18/11 |
Introduction to AI; Chinese Room Problem; Turing Test |
|
2 -Thur, 1/20/11 |
Introduction
to Agents |
|
|
2 |
3- Tues, 1/25/11 |
Agent Environments |
|
4- Thur, 1/27/11 |
Problem Representation State Space Search;
uninformed search |
|
|
3 |
5- Tues, 2/1/11 Quiz |
Quiz Problem Taxonomy Production Systems, heuristics,
informed search |
|
6- Thur, 2/3/11 |
Informed
Search |
|
|
4 |
** Mon 2/7/11 |
Last day to withdraw without record |
|
7- Tues, 2/8/11 |
Non-Classical
Searching |
|
|
8- Thur, 2/10/11 |
Adversarial
Search: Games! |
|
|
5 |
9 - Tues, 2/15/11 |
More
Games |
|
10 - Thur, 2/17/11 |
Computers
and Chess; |
|
|
6 |
11- Tues, 2/22/11 |
Advanced
Gaming Techniques |
|
12- Thur, 2/24/11 |
Review
for Midterm |
|
|
7 |
13- Tues, 3/1/11 |
Midterm
1 |
|
14- Thur, 3/3/11 No Class |
Midterm Break – No Class |
|
|
8 |
15- Tues, 3/8/11 |
Knowledge
Representation, Components
of any logic, KR
in Propositional Logic |
|
16- Thur,
3/10/11 |
Inference
in Propositional Logic: theorem proving and resolution |
|
|
9 |
18- Tues, 3/15/11 |
First
Order Logic; Resolution in First Order Logic |
|
19- Thur,
3/17/11 |
Resolution
in FOL |
|
|
10 |
20- Tues, 3/22/11 |
Introduction
to Prolog |
|
21- Thur,
3/24/11 |
Recursion
in Prolog |
|
|
11 |
22- Tues, 3/29/11 |
Introduction to the Semantic Web: RDF notation |
|
23- Thur,
3/31/11 |
Semantic
Web: SPARQL programming language |
|
|
12 |
** Mon, 4/4/11 |
Last day to withdraw with grade of
‘W’ |
|
24- Tues, 4/5/11 |
Semantic
Web |
|
|
25- Thur,
4/7/11 |
Nonmonotonic Reasoning Systems (JTMS, stable models) |
|
|
13 |
26- Tues, 4/12/11 |
More
JTMS, stable models |
|
27- Thur,
4/14/11 |
Review
for Midterm 2 |
|
|
14 |
28 - Tues, 4/19/11 Midterm 2 |
Test
on material covered since last exam |
|
29- Thur,
4/21/11 |
Easter Break – No Class |
|
|
15 |
30- Tues, 4/26/11 |
Graduate
Projects |
|
31- Thur,
4/28/11 |
Class
Game Competition! |
|
|
16 |
Monday 5/2/11 10:10am –
12noon |
Cumulative AI Final Miriam Hall 207 |
|
Saturday, 5/7/11 |
Graduate Student GRADUATION 12:45pm |
|
|
Sunday, 5/8/11 |
Undergraduate Student GRADUATION 9:45am |
Grading Undergraduate Students (Approximate
distribution of credit):
Midterm #1 – 17%
Midterm #2 – 18
%
Final Exam – 25
%
Homework and Programming Assignments 30%
Quiz 05%
In-Class Grade 05%
(includes class
participation, pop quizzes, and in-class exercises) note: these cannot be made up
Grading Graduate Students (Approximate distribution of credit):
Midterm #1 – 14
%
Midterm #2 -- 15
%
Final Exam – 20 %
\Assignments 25 %
Graduate Assignment 15 %
Quiz 6 %
In-Class Grade 5 %
(includes class participation, pop quizzes, and in-class exercises) note: these cannot be made up
Required
Text:
Artificial Intelligence A Modern Approach 3rd Edition
By, Stuart Russell and Peter Norvig
ISBN # 0-13-103805-2
Graduate Student Project
Graduate students are required to produce a final project for the
course. This entails choosing a topic of
Artificial Intelligence not presented in class (or extending what was presented
in class) and doing the following:
1.
read at
least two articles on the topic
2.
write a
short term paper (4 pages) in your own words describing the topic
3.
write a
software program or hardware project (such as a LEGO robot) demonstrating some
aspect of the topic
4.
present
your paper in a 10-15 minute Powerpoint presentation
to the class
5.
demonstrate
your simulation or demonstration object to the class in a 5-10 minute demo
6.
Submit
term paper, Powerpoint slides,
simulation/demonstration object
Policy on Makeups,
Missed and Late Work:
1. Late
Work: Work will usually be accepted late and
recorded as such. Work is due at the
beginning of class. A 10% penalty is
applied for every class day the assignment is late. No work will be
accepted after solutions have been given out, or after the assignment has been
graded and returned.
2. Make-ups:
Tests are expected to be taken on the test date. Any make-ups must be established with me
ahead of time. There are no make-ups for in-class pop quizzes,
exercises, or participation. To get
these points, you must come to class.
3.
Attendance: Students are expected to come to class. If a class must be missed, however, students
are responsible for all material, assignments, and announcements made during
class. For this reason, you are
encouraged to find a colleague with whom you can communicate to share such
important information.
Programming Conduct Rules:
¨
Programming
assignments are dispensed to reinforce concepts presented in class. Good programming skills comprise a
fundamental component of being a computer scientist. Assignments in this class are short enough
to write by yourself.
As I am trying to endow in you the fundamental techniques and algorithms
of artificial intelligence, no graphical user interface (GUI) is necessary or
required.
¨
Students
may share ideas in composing programs, but may not code them together. There is no sharing of code, only ideas. Any
collaborative work should be acknowledged in the comments. Plagiarizing code will result in a zero for
the program.
Email Communication and Class Computer
Accounts:
·
Email: I prefer to conduct
communication through email. My email
address (as indicated above) is seitzer@.udayton.edu. Please feel free to write me anytime. I try to check my email many times through
the day. If you do not have an email
account, I ask that you get one. Student
email accounts can be acquired from the Systems Administrator. For information, you may call (937)229-3858.
·
Lab Work and Programs: Programming assignments may be written
using the platform of your choice in any lab of your choice so long as the
system on which you are working has an operational C ,
C++, or Java compiler.
Course Web Pages and
Isidore Site:
·
The
course has its own web page that can be found at URL http://homepages.udayton.edu/~jseitzer1/cps480.
The majority of the class work, assignments, and handouts will be posted
on http://isidore.udayton.edu.
Class Email List:
·
Along
with web page postings, I regularly send my classes email via the respective Class Email List. Please make sure you have the correct address
logged with the university to receive all class emails. These lists are maintained by the university.
University of Dayton
Honor Code
The University of Dayton Academic Honor Code: A Commitment to Academic Integrity
I understand that as a student of the University of Dayton, I am a member of
our academic and social community, I recognize the importance of my education
and the value of experiencing life in such an integrated community, I believe that the value of my education and
degree is critically dependent upon the academic integrity of the university
community, and so in order to maintain our academic integrity, I pledge
to:
- Complete all assignments and examinations by the guidelines given to me by my
instructors,
- Avoid plagiarism and any other form of misrepresenting someone else's work as my own
- Adhere to the Standards of Conduct as outlined in the Academic Honor Code.
In doing this, I hold myself and my community to a higher standard of excellence,
and set an example for my peers to follow.
Signed:
Dated: