COSC 6368 --- Artificial Intelligence
( Dr. Eick )
Purpose of this Website
This website intends to satisfy the information requirements of
two independent groups:
- Students that take the graduate AI class
- People that want to find out what AI is,
what is subfields are,
and how its technologies, techniques, and
methodlogies can be used in industrial, government, and
research-oriented environments.
If you have any comments
concerning this website, send e-mail
to: ceick@aol.com
Basic Course Information
Instructor: Dr.
Christoph F. Eick
office hours (PGH 589): MO 4-5p TH 10-11a
class meets: MO/WE 2:30-4p
class room: 315PGH(we moved away from
the terrible 268 PGH class room!!)
Course Materials
Required Text:
- S. Russell and P. Norvig, Artificial Intelligence, A
Modern Approach
- Prentice Hall/Allyn&Bacon, 1995, ISBN: 0-13-103805-2, $62.95
- Call number: Q335.R86 1995
-
Link to Textbook Homepage
Optional books with relevant material:
- N. Nilsson, Artificial Intelligence: A New Synthesis
- Morgan Kaufmann, 1998, ISBN: 1-55860-467-7, $59.95
- Call number: Q335.N495 1998
- E. Rich and K. Knight, Artificial Intelligence, 2nd ed.
- McGraw Hill Book Company, 1991, ISBN: 0-07-052263-4, $71.50
- Call number: Q335.R53 1991
- M.R. Genesereth and N. Nilsson, Logical Foundations of Artificial Intelligence
- Morgan Kaufmann, 1987, ISBN: 0-934613-31-1, $61.95
- Call number: Q335.G37 1988 and Q335.G37 1987
Prerequisites
COSC 4350 or consent with the instructor.
Students that do not have much AI-background
are encouraged to study
chapters 1, 3, and 7 of our textbook prior to August 31, 1999.
Moreover, it is assumed that students taking the class have
basic programming skills (undergraduate data structure (COSC
2320) level
is sufficient). The course project will require programming;
however, students are allowed to choose a programming language
of their own liking to conduct the project.
Material Covered in COSC 6368
Artificial Intelligence(AI) resarch centers on the simulation of intelligence in
computers.
The class gives an introduction to Artificial Intelligence(AI), and
surveys AI technologies, techniques, methodologies, and algorithms.
In
particular, the subfields of AI problem solving and heuristic
search, logical reasoning, knowledge-based
systems, reasoning with uncertain knowledge, and learning will
be covered in more depth by COSC 6368 (see
Organization of our textbook for more details).
Class Transparencies
Here is some information concerning transparencies to be used in the
lectures of COSC 6368:
- Introduction (the material covered in the first week of the
semester will be made avaible in some form (that has not be chosen
yet) no later than October 12, 1999)
- Russel Chapter 3 Transparencies (will be used
in the lecture, skipping pages 40 to 49)
- Additional material backtracking and search in general (will be made
available by Sept. 18)
- Russel Chapter 4 Transparencies (will be used in
the lecture)
- Russel Constraint Satisfaction Transparencies
(not to be used used, but nothing prevents you from looking at those)
- Russel Chapter 5 Transparencies (will be used
in the lecture)
- Russel Intro to Logic Transparencies (only
sections 6.3 and 6.4 of the textbook will be covered in class)
- Russel Intro to Predicate Calculus Transparencies
- Russel Resulution1 Transparencies (subset
to be used in class)
- Russel Resolution2 Transparencies (subset
to be used in class)
- Dr. Eick's additional Transparencies Predicate
Logic and Resolution(Postscript),
more logic transparencies(Powerpoint),
and simple examples
of resolution proofs (Asci)
--- the resolution method will be discussed in
detail in class using a lot of exam
ples. Transparencies will be added to these sets during the
semester.
- Russel Planning Transparencies (will
be used for a short "bird's perspective" introduction to planning)
- Russel Uncertainty Transparencies (subset
will be used in lectures)
- Dr. Eick's Transparencies on "Naive Bayesian
Classifiers" (only transparencies 1, 2, and 13 will be used in the lectures)
- Russel Belief Network1 Transparencies (not
used in the lectures)
- Russel Belief Network2 Transparencies (not
used in the lectures)
-
Slides 1997 AAAI Tutorial by Jack Breese and Daphne Koller on
Belief Networks
and Decision-Theoretic Reasoning. A subset of these transparencies
will be used to introduce belief networks (specifically, transparencies
4, 8, 9, 11, 12, 15, 17, 18, 19-23, 25, 29, 31, 45, 46, 48, 49, 56, 60,
61, 68, 71, 96, 97, 108, 136, 138, 144, 149, 154, 165, and 167
is will be used --- probably,
it is sufficient to just print out those!).
- Black's Introduction to Belief Networks
- Knowledge-based and
Expert Systems
--- instructor transparencies (not
available on the web) and two articles will be available
for copying no later than November 3, so that students can copy
those. Moreover, here are the ppt-transparencies
of
the Shared Ontologies presentation to
be given on Nov. 8, 1999. Moreover, links and other material are
enclosed in
Group
Red's Knowledge-based systems Homepage.
A more detailed discussion of shared ontologies has been given in
Knowledge Sharing and Reuse by
Asuncion Gomez-Perez.
Moreover, a few of Dr. Eick's transparencies on the
RETE algorithm and the subfields of KBS are also available online.
- Group Blue"s
material on decision trees
(containts webpage with teaching material and the 2
benchmarks (in C5.0 format)
to be used for the Decision Tree/NBA Knowledge Discocery Project).
Moreover,
a Powerpoint File is available that contains the instructor's
evaluation of decision tree techniques.
- Additional Teaching Material Machine Learning by
Tom Mitchell (CMU)
- Dr. Eick's Transparencies "Evolutionary Computing"
Lecture, also read John Holland's Scientific American article that was
distributed in class! Moreover, if you like to know more about
evolutionary computing check out the Evolutionary
Programming Course Website (COSC 6367) that contains
more material and especially links to other
EC-websites.
- Dr. Eick's "Final Thoughts on COSC 6368
and AI in General"-Transparencies (in Powerpoint)
The Russel transparencies can also be obtained by following the
instructor link from the textbook link, and then clicking the slide link.
AI-Links
Dyer's AI-demo Listing
List of AI-demos
AI on the
Web (Russel's list)
John
McCarthy (Inventer of LISP) on what it AI (not everyone will
agree with everything he has to say)
Schedule for Lectures
One goal of this class is to give you a very up-to-date introduction
to AI.
Due to the fact that a new textbook is used, and due to the
fact that I havn't taught the graduate AI-class for 8 years, it
is very difficult for me to give you a good schedule for Fall 1999.
The enclosed schedule is my best guess of what will be covered
in class, and the schedule will likely be modified several
times during the course of the semester.
To my best knowledge chapters 1, 3, 4, 5, 7, 9, 10, 14, 15, 18,
and 27 of the Russel textbook will be covered indepth. Chapters
6, 8, 11, 16, 19 will be partially covered by COSC 6368. If there is
enough time left at the end of the semester, chapter 23 will be
also
covered. Additionally, a few journal articles and transparencies of the
instructor will be used as teaching material. Moreover,
frequently, examples will be discussed in the lectures that are not
contained in the listed teaching material. A preliminary schedule
for COSC 6368 is listed below.
Remark: All references to chapters refer to the Russel textbook!
| Date | Topic | Reading
|
|---|
| Aug 23
| Class Information
| Instructor Transparencies
|
| Aug 25
| Introduction AI
| Instructor Transparencies
|
| Aug 30
| Search
| Chapter 3
|
| Sep 1
| Search + Homework1
| Chapter 3
|
| Sept 8
| Heuristic Search + Chess Problem
| Chapter 4
|
| Sept 13
| Heuristic Search
| Chapter 4
|
| Sept 15
| Heuristic Search + Homework1
| Chapter 4
|
| Sept 20
| Game Playing
| Chapter 5
|
| Sept 22
| Intro/Review Logical Reasoning
| Chapter 6.3+6.4+7
|
| Sept 27
| FOL and Situation calculus
| Chapter 7
|
| Sept 29
| Inference in FOL
| Chapter 9 + 10.2
|
| Oct 6
| Resolution + Homework2
| Chapter 9
|
| Oct 11
| Resolution + Group-Tasks + Default Logic
| Dr. Eick's transparencies + 10.8
|
| Oct 13
| Prolog + Introduction to Planning
| Russel 10.3 + Subset Chapter 11.1-11.6
|
| Oct 18
| Introduction Uncertainty
| Chapter 14 + ...
|
| Oct 20
| Belief Networks I
| Chapter 15 Breese/Koller tranparencies
|
| Oct 25
| Left-Overs + Review Midterm Exam
|
|
| Oct 27
| Midterm Exam
|
|
| Nov. 1
| Belief Networks II
| Chapter 15.6
|
| Nov. 3
| Homework3 + Knowledge-based Systems I
| Instructor Transparencies
|
| Nov 5,3p
| Knowledge-based Systems II --- Rule-based Programming
| Instructor Transparencies +
Bastani/Eick Article "Knowledge Engineering" to be part of
Webster's Encyclopedia of Engineering.
|
| Nov 8
| KBS III --- Shared Ontologies and Frame-based Systems
| IEEE Intelligent Systems Special Issue "Coming to Terms
with Ontologies, pp. 18-26, Jan./Feb. 1999 + 10.6 Russel
|
| Nov. 15
| Introduction to Inductive Learning
| Russel Chapter 18
|
| Nov. 17
| Decision Trees + Homework4
| Chapter 18
|
| Nov. 22
| Course Project, C5.0 Green's "Good AI-Demos"
Presentation
| Green's Webpage
|
| Nov. 24
| Leftovers + Evolutionary Computation
| Holland's Scientific American Article + Russel Chapter
15
|
| Nov. 30
| Neural Networks + Ret. H1+3+MT + Course-Evaluation
| Subset Russel chapter 19
|
| Dec. 1
| History and Future of AI
| IEEE Computer "25 years of Computing" Special
Issue, "The Challenge of AI", pp. 86-98, Oct. 1996. + Chapter 27
|
| Dec. 10, 2p
| Final Exam
|
|
|
COSC 6368 Group Activities
Students in the class are subdivided into 4 groups. The role of the
groups is to conduct www-inquiries to improve COSC 6368, to find, install, and
test AI-software and demos on the web, and to do things that are
helpful to your class mates (members of the other groups), and to students
that will take this class in the year 2000 or later. Each group produces
a web-page that summarizes their findings. This semester's
groups are (the last named student in the list
is the "coordinator" of the group), and their
specific tasks have been described below:
- Black: Tesfaye Biru, Chin-Fu Chou,
Qiaomei Huang, and Wenqing Huang. Task: Checkout how students
can use the PC-based belief network tool Netica, Hugin,
or MSBN (or other
belief network tools
--- see Classification
Tools (look under Bayesian Tools)
for course homework3;
members of this group will also identify websites that have "good"
teaching material on Belief Network. final product: webpage to be
delivered no later than Nov. 4.
- Blue:
Evgueni Kaniachine, Sin-Chieh Liu, Kyle Mickelson, and Manu Murali. Task:
Find teaching material and websites that discuss decision trees; look for
free decision tree tools (checkout
C5.0 free download
for scaled down version) that are available on the web. final product:
2 C4.5 processable datasets and webpage to be delivered on Nov. 9.
- Green:
Leila Naas, Pandurenga Narayanasamy, Nedialka Petkova, Lavanya Prabu, and
Waree Ringsurongkawong. Task: Find good AI demos on the web, rank
them, and share
what you found by giving a short presentation in the Thanksgiving week ---
also develop a webpage that can be linked the COSC 6368 webpage.
final product: results to be presented in 15 minute presentation during
class on Nov. 22 and website to be submitted no later than Nov. 24.
- Red:
Edward Stangler, Charoench Sutippantupat,
Kaladhar Yeddanapudi, and Chandler
Wilkerson. Task: Look for websites that contain useful
teaching material, demos, and scientific publications centering on
shared ontologies and knowledge-based systems. final product:
transparencies to teach RETE-algorithm, shared ontologies,
and frame-based systems to be submitted by October 30; webpage
to be submitted no later than November 4.
Grading
The course will have open-book midterm and final exams, and 4 assignments that contain paper&pencil-style questions, 2
problems that require programming, and one essay-style
problem. Each student has to have a weighted average of 74.0 or higher in the
exams of the course in order to receive a grade of "B-" or better
for the course.
Students will be responsible for material covered in the
lectures and assigned in the readings. All homeworks and
project reports are due at the date specified. However, students
are premitted to turn in a single homework 4 days late. However,
there will be no extensions for the last homework (homework 4).
No late submissions
will be accepted after
the due date. This policy will be strictly enforced.
Course grades will be based on 34% final exam, 27% midterm
exam, 36% for the assignments, and 3% will be allocated
to group activities.
Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-78 B-:78-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0
Only machine written solutions to homeworks and assignments
are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our
only source of information is what you have turned in. If we are not capable to understand your
solution, you will receive a low score.
Moreover, students should not throw away returned assignments or tests.
Students may discuss course material and homeworks, but must take special
care to discern the difference between collaborating in order to increase
understanding of course materials and collaborating on the homework /
course project
itself. We encourage students to help each other understand course
material to clarify the meaning of homework problems or to discuss
problem-solving strategies, but it is not permissible for one
student to help or be helped by another student in working through
homework problems and in the course project. If, in discussing course materials and problems,
students believe that their like-mindedness from such discussions could be
construed as collaboration on their assignments, students must cite each
other, briefly explaining the extent of their collaboration. Any
assistance that is not given proper citation may be considered a violation
of the Honor Code, and might result in obtaining a grade of F
in the course, and in further prosecution.
Communication with the teaching staff
We strongly encourage students to come to my office hours or
to talk to me directly after class.
If a homework clarification is posted after a student has completed an
assignment, the student should contact us as soon as possible to check if
the assumptions s/he made are going to be accepted.
Please do not e-mail us with grading questions. If you want
us/me to
explain why I took points off, you can talk to me/us
during office hours and directly after class.
Concerning questions concerning the homework grading in Fall'99, please
talk to me during my officehours (or make an appointment) between November
17 and Dec. 6.
COSC 6368 Fall 1999
Problems Homework1(the specification
was updated significantly
on Sept. 14; minor additions were made on Sept. 15)
Input File of Training Benchmark (contains 12
examples of training benchmark); some errors have
been corrected on Sept. 15, if there are any other
errors, please send me an e-mail!
Problems Homework2
Midterm Exam Fall 1999
Problems Homework3+4 (RETE Problem
corrected on Nov. 16, 9a, and problem 16 updated on Nov. 19)
C5.0 Tutorial
(C5.0 is a decision tree machine learning and knowledge discovery
tool --- probably, the most famuous one of the decision tree family)
Course Exams
Midterm
The midterm will cover material covered through October 18,
and will be given on
Wednesday, October 27 during
the regular class hours. Here is the
Review List for the Midterm Exam. The
exam will be "open textbooks".
Results Midterm Exam (preliminary!!!)
Translation from test points to number grades
Final Exam
The final will be held Fr., Dec. 10, 2-5p.
Here is the Review Sheet for
the COSC 6368 Final Exam.
AI Qualifying Exam
The AI Qualifying Exam will consist of two parts:
part1 with be the final exam of COSC 6368. Part2 will
be an extra exam that has been scheduled for Mo., Dec. 13, 4:20p.
Part2 of the qualifying exam will cover the following areas:
- Heuristic Search and Game Playing (chapters 3-5, focusing on
material covered in the lectures)
- Logical Reasoning and Resolution (focusing on material that
was discussed in the lectures of COSC 6368)
- Planning (read chapters 11-13 of the textbook)
- Machine Learning (read chapters 18-20 of the textbook)
Part2 will take approx 75 minutes; Part1 of the qualifying exam has
a weight of 60% and Part2 has a weight of 40%. Both exams are
open textbook!
Schedule and Deadlines
| When? | Topic | Due Date | Weight
|
|---|
| Sept. 1, 99 | Homework 1: Heuristic Search | September 28, 99 | 130/400*36%
|
|---|
| Sept. 27, 99 | Homework 2: Logical Reasoning | October 12, 99 | 54/400*36%
|
|---|
| Mo., Oct. 4, 99 | no class!!
|
|---|
| October 25, 99 | Review Class Midterm Exam |
|
|---|
| October 27, 99 | Midterm Exam | | 27%
|
|---|
| - | Group Projects | | 3%
|
|---|
| October 25, 99 | Homework 3: Uncertainty, Belief Networks, and
Knowledge-based Systems |
Nov. 17, 99 | 113/400*36%
|
|---|
| Nov. 8, 99 | Homework 4: Machine Learning, Decision Trees, and
Knowledge-based Systems | Nov. 30, 99 | 103/400*36%
|
|---|
| Fr., Nov. 5, 3:00-5:30p | Makeup Class |
|
|---|
| We., Nov. 17 | 2:30-3:40p: regular class; 3:45-3:55p: discussion
of qualifying exam |
|
|---|
| We., Nov. 11, 99 | no class!!
|
|---|
| Fr., Dec. 10, 99 2-5p | Final Exam | | 34%
|
|---|
| Mo., Dec. 13, 99, 4:20p | Qualifying Exam Part2 | Final
Exam is Part 1
|
|---|
Stanford
Page with Additional Course Material
Discussions of Homework Problems
own-red-car-rich-problem:
VpVc (owns(p,c) ^ red(c) ^ car(c) -> rich(p))
versus
Vp]c (owns(p,c) ^ red(c) ^ car(c) -> rich(p))
COSC 6368 News (11/29)
- I will not have regular office hours in December; my officehours
in December 1999 are as follows: Fr. Dec. 3, 3-4p, We., Dec. 8, 4-5p,
Fr. Dec. 10, 11a-noon, Th., Dec. 16, 10-11a, Th., December 30, 10-11a.
- The AI'99 Homework Scores
(Chess Endgame Grading Guidelines).
have been updated and now contain the scores of homeworks 1 and 2
and of the telescope problem. I will do further grading on
Dec. 3 and, if necessary on Dec. 8. If you like, you can pick up
your homeworks during my Dec. 8 office hour; otherwise, homework3+4
will be returned to you after the final exam on Fr., December 10!
- In order to grade the Huntington disease homework I need your
Huntington Disease Belief Network. Please mail your belief network
as an e-mail attachment (identify yourself in the e-mail message
and let me know what belief network tool you used)
no later than Th., Dec. 2, noon to: ceick@aol.com
- Due to the fact that almost all COSC-graduate
classes give their final exam this week, the deadline
of Homework4 has been extended as follows:Submit your
Decision-Tree/NBA report no later than Sa., Dec. 4, 9p to ceick@aol.com.
Submit the hardcopy of the report, and the solutions to the
other homework problems on Tuesday, Dec. 7 no later than 3p!
- The grading of the midterm exam has been completed.
The preliminary results can be found in the Midterm subsection
of this webpage; it will be discussed and returned to students
in the Dec. 1 class.
- Reading Schedule Fall 1999:
- Read chapter 1 (skipping history of AI-part) thr. Aug. 29, 1999
- Read chapter 3 by August 29, 1999
- Read chapter 4 by September 7, 1999
- Read chapter 5 by Septeber 15, 1999
- If your knowledge of logic is soft read sections 6.3 and 6.4 by
Sept. 20.
- Read section 7.1, 7.2, and 7.3 by Sept. 21
- Read sections 7.6, 7.7, 7.8 by Sept. 23
- Read chapter 9 by Sept. 29
- Read sections 10.1, 10.2 and 10.8 by October 5.
- Read chapter 11 by October 11.
- Read chapter 14 by October 15.
- Read chapter 15 by October 20
- Read "Ontology Paper" by November 8.
- Read chapter 18 by Nov. 14.
- Look at Prof. Mitchell's Decision Tree Transparencies by Nov. 16.
- Read C5.0 manual by Nov. 20.
- Read Holland's Genetic Algorithm arcticle by Nov. 21.
- Read the last chapter of our textbook by Nov. 30.
- Read AI-article from Special Issue "25 years IEEE" by Nov. 30.
Accesses since July 21, 1999:
last updated: November 29, 1999, 9:06a
And finally: Frogland --- all about frogs