CSC529 - Artificial Intelligence - Spring 2006

Department of Mathematical Sciences, University of North Carolina at Greensboro

Instructor: Dr. Nancy Green, 322 Bryan, phone: (336) 256-1133
Class Meetings: Tu/Th 11:00-12:15 pm
Instructor's Office Hours: 330-430 Tu/Th, and by appointment
Course web page: lookup on http://www.uncg.edu/~nlgreen
Course Description Course Calendar Grading Policies Resources Project

Course Description

 Prerequisites:Grade of at least C in CSC330 (or equivalent if transfer student). The course is designed for junior-senior level undergraduate computer science majors or minors, although graduate students that meet the prerequisites may enroll in the course.  Skills developed from previous courses in formal logic and discrete mathematics will be useful.  CSC339 (which covers Prolog) would be helpful but is not required. 

Brief Description:an overview of the theoretical foundations and main areas in Artificial Intelligence (AI) today.  In addition the course will provide programming experience in AI problems.  Students will be given several short programming assignments using Prolog and a larger project using Prolog or possibly other AI tools.  Students will be expected to explain the AI methods used in their project and give a demonstration of the project.  In addition, graduate students are expected to read a research paper on an application of AI and to make a presentation to the class on the paper.  Also, graduate students' examinations may contain different questions. 

Student Learning Outcomes: By the end of the course, the student 1) should be able to demonstrate understanding of the basic concepts, methods, and algorithms of Artificial Intelligence covered in the course, and (2) should be able to implement programs (using Prolog and possibly other AI tools) implementing AI methods and algorithms.  In addition, graduate students should be able to locate, evaluate, and communicate information presented in the related technical literature. 

Topics:

Required Textbook: Computational Intelligence: A Logical Approach, by Poole, Mackworth, & Goebel. Oxford U. Press, 1998. [Lectures from book downloadable here]  Bring your textbook to class.

Optional Textbook:
Prolog Programming for Artificial Intelligence, by Ivan Bratko, 3rd ed. Addison Wesley, 2001.[codedownloads]

Handouts: You are also responsible for reading supplementary materials handed out in class or placed on-line for the clas

Course Calendar
(Note: this is a tentative schedule. It is your responsibility to check the web site for updates during the semester.)
 Tu/Th dates Due Dates/Tests Textbook
Reading		 Topic this week 
Other 
Jan 10/12
ch. 1 Introduction to AI; begin Logic Programming (LP) lectures (optional) Bratko ch. 1-3, 5-7, 8.4, 9.1; or other Prolog book 
Jan  17/19
ch. 2
 finish intro to LP; begin Logical Foundations
Jan 24/26 HW 1 due 1/26
 ch. 3
 finish Logical Foundations; more on LP
Jan 31/Feb 2
ch. 4
 Search
Feb 7/9 Test 1 Thurs Feb 9


Feb 14/16
ch. 5
 Knowledge Representation, Expert Systems
 Coppin reading on Expert Systems
Feb 21/23
ch. 6
 Metainterpreters
Feb 28/Mar 2 HW 2 due 2/28
 ch. 9
 Default reasoning
Mar 7/9 No class (spring break)



Mar 14/16
ch. 10
Reasoning under uncertainty (Belief networks and decision networks)
 Handouts: Bratko on Bayesian network (365-72), my baseball example, Korb & NIcholson (p. 31)
Mar 21/23 Part I of project due Tues 3/21 (extended date)
Test 2 Thurs 3/23 


Mar 28/30 Class 3/28 alternative (meets  3/28, 2-3:15  in 121 Bryan)
Class 3/30 alternative (TBA)


Apr 4/6
ch. 11 (up to p. 408, and 429-432)
 Machine learning (Introduction to ML, decision trees, naive Bayes)
Apr 11/13 HW3 due 4/13
 ch. 8
Planning
 Handouts: my STRIPS example, Bratko on STRIPS planner (p. 414-21)
Apr 18/20 Test 3 Thurs 4/20 
Plan Recognition (not in CI book)
Apr 25/27 Project due 4/25 (demos continued on 4/27)


May 2 No class (Friday classes meet today)



Grading
Description Points (Undergraduate students) Points (Graduate students)
Test 1 25 25
Test 2 25 25
Test 3
 15 15
HW 1 5 required but no points
HW 2 5 5
HW 3 5 5
Project 20
 20
Presentation (graduate students only) not applicable 5
Miscellaneous (extra credit)
 (in-class assignments, class participation)
 5

Course Policies

Attendanceis required. You may be dropped from the course for missing more than six classes.

Academic Integrity:You are expected to read and follow the UNCG Academic Integrity Policy. All work should be the student's own work unless otherwise specified in the instructions for an assignment.

Distracting/disruptive behavior  is not conducive to maintaining a good classroom environment for learning. Engaging in behavior during class such as cell phone use, use of laptops for non-class-related activities, private conversations, arriving late or leaving early (unless you have made arrangements with the instructor), and other non-class related activities may result in a request to leave the classroom. Persistent behavior of this type may result in being dropped from the course (see UNCG Disruptive Behavior Policy).

Late Work: Late work on HW1-HW3 will be penalized at 2 points per day (each day ending at 5 pm, weekends and holidays included), but will not be accepted after the assignments have been graded or the solutions have been discussed in class.  If you are unable to attend on a date when work is due it is your responsiblityto have your work delivered to the instructor for credit. Scheduled graduate student presentations that are late may not receive credit.  Late work on the project will be penalized at 5 points per day and must be demoed at a time approved by the instructor in order to receive credit.

Missed exams may be taken only if the student's absence has been excused by the instructor and if the exam is made up at the make-up exam time announced by the instructor.

Samples of student work(assignments and tests) may be shown to reviewers for departmental accreditation. 

Resources

  Prolog