CSCE 4613 Artificial Intelligence – Fall 2009
Tentative Syllabus
General Information
- Class
Schedule: TR9:30-10:50am - Location: JBHT 234
- Prerequisite:
Data Structures
- Required
Text: Ben Coppin, Artificial Intelligence Illuminated,
Jones/Bartlett Publishers, Sudbury, MA,
2004 ISBN 0-7637-3230-3
- Instructor:
Dr. Craig Thompson, http://csce.uark.edu/~cwt/,
phone: 479-575-6519, email: cwt@uark.edu
- Office
hours: TR 11:00-12:00
or by appt, JBHT 516
- Class
Web Site: http://csce.uark.edu/~cwt/COURSES/2009-08--CSCE-4613--AI/syllabus.html
- Project
Web Site: http://vw.ddns.uark.edu
- Catalog
Description: Introduction to
intelligent agents, AI languages, search, first order logic, knowledge
representation, ontologies, problem solving, natural language processing,
machine vision, machine learning, and robotics.
- Artificial
intelligence (AI) is the computational study of intelligent
behavior. AI has connections to several disciplines including
Computer Science, Philosophy, Psychology, Linguistics, Game Theory,
Engineering, Neurobiology, and Sociology. You will learn how to tell
a person from an android (the Turing Test); how to represent knowledge;
how to get computers to reason, see, hear, speak, and answer questions;
whether machines have feelings; how teams of autonomous robots can work
together; and what the world will be like when "everything
is alive." One way to study AI is to imagine a world
composed of agents that can communicate and reason – some of those
agents may be humans but anything can become an agent. One vehicle
for considering this world is to model it using 3D virtual world
technology, in particular, Second Life/Open Simulator – for example,
see paper on Modeling
Healthcare in a Virtual World..
Schedule
It is recommended that, before class, you read (skim) as much as you can of the indicated sections (C
= Coppin). Generally, with some exceptions, Tuesdays are lectures and Thursdays are project sessions and discussions on
discussions on Second Life (SL), project ideas, term project status or
interesting topics.
|
Date
|
Topic
|
Reading
|
Homework
|
|
08/25-T
|
Syllabus,
AI History, Foundations, Scope
|
C1,
C2, AI links, SL project, SL demos, ant
demo
|
|
|
08/27-R
|
Term
projects: SL/OS
building and scripting
|
SL Wikipedia, SL website, research,
SL building, scripting, scripting
|
|
|
09/01-T
|
Agents,
Pervasive Computing, RFID agent middleware
|
C19,
EiA, Soft
Controllers, RFIDmw
|
HW #1
|
|
09/03-R
|
Term
projects: term project theme and discussion
|
SL recent progress
|
|
|
09/08-T
|
AI
Languages – Prolog, Lisp
|
Prolog, Lisp1,
Lisp2
|
|
|
09/10-R
|
Demo #1 in
class
|
|
HW
#2 – demo 1
|
|
09/15-T
|
Natural
Language Processing
|
C20,
Kyle’s slides, MBNLI,
Spatial
|
Bus
Fair
- 9/15 11-3pm
|
|
09/17-R
|
Term Project
in class; Visit RFID Center at 2 map
|
|
|
|
09/22-T
|
Machine
Vision
|
C21
|
Engr
Fair - 9/22 2-6pm
|
|
09/24-R
|
Term projects
– project selection spreadsheet
|
|
HW
#3
|
|
09/29-T
|
Search
|
C4,
C5
|
|
|
10/01-R
|
Term projects
|
|
|
|
10/06-T
|
Game
Theory
|
C6
|
|
|
10/08-R
|
Term projects
|
|
|
|
10/13-T
|
Exam I – review, hand out take home
exam
|
|
|
|
10/15-R
|
Term projects
|
|
Take
home Exam I due
|
|
10/20-T
|
Knowledge
representation, Ontologies
|
C3,
Protege
|
|
|
10/22-R
|
Term projects
|
|
|
|
10/27-T
|
Problem
Solving, Planning, Reasoning
|
C15,
C16
|
|
|
10/29-R
|
Demo #2 in class, report
|
|
HW #4 –
demo 2, report
|
|
11/03-T
|
Logic,
Propositional & Predicate Calculus, Inference, Resolution, Unification
|
C7,
C8
|
|
|
11/05-R
|
Term projects
|
|
|
|
11/10-T
|
Rules,
Expert Systems
|
C9
|
|
|
11/12-R
|
Term projects
|
|
|
|
11/17-T
|
Fuzzy
Reasoning
|
C18
|
|
|
11/19-R
|
Learning
– Neural Networks, Genetic Algorithms
|
C11,
C14, neural
nets, genetic
algorithms
|
|
|
11/24-T
|
No class
|
|
|
|
11/26-R
|
Thanksgiving
Holiday
|
|
|
|
12/01-T
|
Exam II – review, hand out take home
exam
|
|
|
|
12/03-R
|
Term projects
|
|
Take home Exam
II due
|
|
12/08-T
|
Demo #3 in class, report
|
|
HW
#5 – demo 3, report
|
Assignments/Evaluation
The course grade will be based on:
Two Exams
- Exam
I and Exam II – 25% each – The first exam will
cover material from the first half of the course; the second exam will
cover material from the latter half. The exams will be open-book,
open notes, and take home – work alone! No makeup exams are
given. If you believe I have made an error in grading your exam,
please bring the matter to my attention as soon as possible. If you have a question about an exam question, bring
this to my attention during the exam period. Study Guides: Exam I review and Exam II review
Homework is due by email to Dr. Thompson cwt@uark.edu on or before class on the date due
– see syllabus
- HW
#1 – 3% – Each student will complete:
- Survey - Getting
to know you
- Survey -
Virtual Worlds and Gaming
- Resume
– send file to cwt@uark.edu - example
- SL
avatar name – visit Second Life;
under Getting Started, choose and remember SL avatar name and pwd;
use Acxiom Lab Macs or PCs and login to SL client (or download free SL
client to your own late model laptop and login); explore newcomer island
(learn to walk, fly, edit your avatar, browse your inventory folder, use
map, teleport). Avoid mature content and violence on UARK
machines. Use Search to visit “University of
Arkansas” island; send email to Keith Perkins
<ksp03@uark.edu> cc Dr. T <cwt@uark.edu> for access to
our SL island; you will receive an email explanation and SL note card
inviting you to join group “University of Arkansas Campus
Users” group and will be given building and scripting
privileges.
- OpenSim
avatar name – login to OpenSim … procedure TBD
- HW
#2 – 5% – Hello World - Build/Script in OS – Each
student will individually build and script something interesting in
SL – demo in class on 9/10 – theme is smart world
healthcare at the hogspital …
- HW
#3 – 2% – Term Project Ideas – send instructor
email prioritizing 5 project ideas (best first). One short paragraph
per idea. Limit 1 page.
- HW
#4 & #5 – 20% each –Two Term Projects -
Deliverables include the in-class demo and a zip file containing a final report
and code – enough info so the next student can pick up and build on
your contribution. Be sure to turn off protections on all prims and
scripts so they are reusable. We will discuss ideas in class and
instructor will make final assignments.
- Grad
Students – Each grad student will prepare and teach one lecture
marked – topics, e.g., neural nets, genetic algorithms.
Grading scale different than undergrads on exams.
Course Policies
Course Web Page and Email List
Class announcements will be posted on the course syllabus
(this page) and/or the course email list. Watch for frequent updates.
Conduct
Come to class on time as a courtesy to your
professor and fellow students.
Homework and Programming Due Date Policy
Homework (including homework assignments and programming
assignments) is due in class on the dates specified in the course schedule.
Homework is worth full credit when turned in at the beginning of the class on
the due date. A 10% penalty per day will be incurred for late homework.
No submissions will be accepted after the solutions are posted.
Academic Integrity
The work you submit for this class is expected to be the
result of your own work in your own words. You are free to discuss
course material and general approaches to problems with others but you should
never misrepresent someone else's work as your own. It is also your
responsibility to protect your work from unauthorized access. If you quote, be
sure to use quotation marks.
Accommodation for Disability
If you have a disability that will impact your work in this
class, please contact me to discuss your needs.
Inclement Weather Policy
If the University announces closure due to inclement weather
(which is announced on http://www.uark.edu),
then we will not have class that day. Even if the University is open:
- if
the instructor (who lives on a hill) cannot make it to class (due to ice),
then he will send an email to class members and also post a note on this
web page BY 7AM indicating NO CLASS TODAY (<DATE>) and any
instructions for assignments. He will also alert the CSCE Department
staff (but that will be after the office opens at 8am)
- if a
student feels it is unsafe to come to class due to inclement weather, then
s/he should (at earliest convenience) send an email to the instructor
indicating the reason for missing class.
- In
the above cases, any assignments due that day will be due the next class
period.
