CSCE 4613/5043 Artificial Intelligence – Spring 2008

Tentative Syllabus

General Information

• Class Schedule:  CSCE 4613/5043 - Artificial Intelligence - Time: TR8:00-9:20am - Location:  JBHT 147
• Prerequisite:  CSCE 2143 Data Structures
• Required Text: Ben Coppin, Artificial Intelligence Illuminated, Jones and 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 12:00-1:00, JBHT 516
• Class Web Site:    http://csce.uark.edu/~cwt/COURSES/2008-01--CSCE-4613-5043--AI/syllabus.html
• Catalog Description: 
• CSCE 4613 Artificial Intelligence - Introduction to intelligent agents, AI languages, search, first order logic, knowledge representation, ontologies, problem solving, natural language processing, machine vision, machine learning, and robotics.
• CSCE 5043 Artificial Intelligence - In-depth introduction to AI. Topics include: philosophical foundations, cognition, intelligent agents, AI languages, search, genetic algorithms, first order and modal logic, inference, resolution, knowledge representation, ontologies, problem solving, planning, expert systems, uncertainty, probabilistic reasoning, fuzzy logic, machine learning, natural language processing, machine vision, 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."

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 Second Life (SL), project ideas, project status or interesting topics.

Date	Topic	Reading	Homework
01/15-T	Syllabus – Intro: History, Foundations Perspectives, Scope	C1, C2, AI on the web	fun
01/17-R	SL Intro & Communicating (Bailey)	SL wikipedia, web, tutorials, research
01/22-T	Agents: Pervasive Computing, Everything is Alive, middleware, SOAs, RFID agent middleware (Hoag)	C19, EiA, Soft Controllers, RFIDmw, RFID links, botanicalls, ants in SL	HW #1 due
01/24-R	SL Scripting LSL (McFarlane) , Discuss ideas for term projects	Josh’s slides, SL scripting, scripting
01/29-T	Knowledge representation, Ontologies	C3, Protege
01/31-R	SL Building (Bailey) , Discuss ideas for term projects	SL building
02/05-T	Natural Language Processing (Neumeier)	Kyle’s slides, C20, MBNLI, Spatial
02/07-R	Visit RFID Center - map
02/12-T	Search	C4,C5	Career Fair – 2/12-13
02/14-R	HW#2: SL Demo - scripts and builds		HW #2 due
02/19-T	Game Theory	C6
02/21-R	No class – instructor sick
02/26-T	HW#3: AI research paper reports in class		HW #3 due
02/28-R	Exam I review
03/04-T	Exam I		HW #4 due 3/2
03/06-R	SL Assignments
03/11-T	Problem Solving, Planning, Reasoning	C15, C16
03/13-R	SL Status Report I class, Propositional and Predicate Logic	C7
03/18-T	Spring Break
03/20-R	Spring Break	Term Project – status reports
03/25-T	Inference, Resolution, Unification	C8
03/27-R	SL Demo #2		HW #5 due
04/01-T	Rules, Expert Systems	C9
04/03-R	Fuzzy Reasoning	C18
04/08-T	AI Languages – Prolog, Lisp (grads)	Prolog, Lisp1, Lisp2
04/10-R	SL Demo #3		HW #6 due
04/15-T	Machine Vision	C21
04/17-R	Machine Vision (Cothren)
04/22-T	Learning – Neural Networks, Genetic Algorithms (grads)	C11, C14
04/24-R	SL FINAL DEMO		HW #7 due
04/29-T	Exam II review
05/01-R	Exam II

---

Assignments/Evaluation

The course grade will be based on:

• 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.  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. Study Guides: Exam I review and Exam II review
• HW #1 – 5% – Each student will complete:

(a) Survey - Virtual Worlds and Gaming

(b) Survey - Getting to know you

(c) resume – send file to cwt@uark.edu - example

(d) login to Second Life (use graphics-capable Acxiom Lab Macs or your own high-end laptop), get SL in-world user name, explore newcomer island (learn to walk, fly, edit your avatar, browse your inventory folder, use map, teleport). Avoid mature content on UARK machines. Use Search to join group “University of Arkansas Campus Users.” Instant message Tulenian Melnik (aka Joshua McFarlane jwmcfar@uark.edu) for access to our SL island.

• HW #2 – 5% – Build/Script in SL & List Ideas – Each student will complete:

(a) individually build and script something interesting in SL – demo in class on 2/14

(b) prioritize 10 project ideas (best first). One paragraph per idea. Limit 2 pages.

• HW #3 – 5% – Research Paper – Each student will

(a) select and read one recent AI research paper,

(b) write a 1-page single-spaced summary to be handed in (use this paper format)

(c) 2-minute presentation in class. 

Go to the library and find AI conference proceedings.  Or login to the university library (http://library.uark.edu/patroninfo/, use your UARK id and password), search for online: keywords: artificial intelligence, agents, fuzzy, expert systems,   journal, conference, etc.

• HW #4 – 0% –Term Project – Identify and Select Areas of Interest – due 3/2 by email
• HW #5, 6, 7 – 30% – Term Project – Demos 1, 2, 3. Deliverable is in-class SL demo on 3/27, 4/10, 4/24.
• Grad Students – Each grad student will prepare and teach one lecture marked – topics: lisp, prolog, neural nets, genetic algorithm. 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.