Contact Information
| Instructor: Dr. Nancy Green | Office Hours: Tu/Th 4:45-5:30 and by appointment |
| Email: nlgreen@uncg.edu | Office: 322 Bryan |
| Phone: (336) 334-5836 |
Prerequisites: grade of at least "C" in CSC 330. 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 algorithms will be useful. CSC339 (which covers Prolog) would be helpful but is not required.
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 and algorithms used in their project and give a demonstration of the project. In addition, graduate students are expected to write a report on an application of AI based on sources in the research literature and to make a presentation to the class on their report. Also, graduate students' examinations may contain different questions.
Required Textbook: Computational Intelligence: A Logical Approach, by Poole, Mackworth, & Goebel. Oxford U. Press, 1998. [Comes with CISpace applets and lecture overheads.]
Supplementary Textbook (optional): Prolog Programming for Artificial Intelligence, by Ivan Bratko, 3rd ed. Addison Wesley, 2001. [Comes with prolog examples.]
Student Learning Outcomes: By the end of the course, students (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:
Late Work: Late work on HW1-HW3 will be penalized at 1 point per day (each day ending at 5 pm, weekends and holidays included), but will not be accepted after the assignments are returned or the solutions have been discussed in class. Late work on HW4-HW5 and in-class presentations (all student project demos and graduate student reports) will not be accepted. (If you are unable to attend on a date when work is due it is your responsiblity to have your work delivered to the instructor for credit.)
Missed Tests: Tests missed due to severe illness or other emergency situations may be made up if (1) the absence is excused by the instructor before the exam is graded, (2) appropriate written documentation of the illness or situation (specified by the instructor) is provided when the exam is made up, and (3) the exam (or alternate work specified by the instructor) is made up by the date specified by the instructor. Note that it is the student's responsibility to satisfy these conditions. An exam that is not made up will receive a grade of 0.
Attendance: Attendance is expected. If you miss more than 5 classes (not counting days on which tests are given or days when class was officially cancelled due to winter storm, etc.) you may be dropped from the course. (If you are dropped after the last day to withdraw without academic penalty you may receive WF.)
Grading: The final course grade will be assigned based upon the following factors:
| Brief Description | Undergraduates only | Graduate students only |
| Test 1 | 20 | 20 |
| Test 2 | 20 | 20 |
| Test 3 | 20 | 20 |
| HW1 | 5 | 5 |
| HW2 | 5 | 5 |
| HW3 | 5 | 5 |
| HW4 (Project design & prototype) | 5 | 5 |
| HW5 (Project implementation & demo) | 10 | 10 |
| Extra report and presentation | Not applicable | 10 |
| Total | 100 | 110 (graduate students grade will be computed by dividing points earned by 110) |
Samples of student work (tests and assignments) may be shown to reviewers
for department accreditation.
| Week (T/Th) | Topic | Related reading in textbook or handouts | Tests/Due Dates/ Miscellaneous |
| Jan 14/16 | Introduction | ch. 1 | |
| Jan 21/23
Jan. 22 is last day to drop course with refund |
Logical Foundations (LF) | ch. 2 | UNCG was closed Jan 23 due to winter weather |
| Jan 28/30 | LF continued | HW1 due date ofJan 30 has been changed to Feb 6 | |
| Feb 4/6 | Logic Programming | ch. 3 (except 3.7), optional: Bratko ch. 1-3, 5-7, 8.4, 4.5, 9.1 | HW1 due Feb 6; graduate students should sign up this week for date to give report |
| Feb 11/13 | Search | ch. 4.1-4.5, optional: Bratko ch. 11 | |
| Feb 18/20 | Search (cont.), Knowledge Representation | ch. 5 | HW2 due Feb 18 |
| Feb 25/27 | Knowledge Engineering & Expert Systems | ch. 6 | Feb 27: Test 1 covers ch. 1-4 |
| Mar 4/6 | continued | my expert systems lecture notes; optional: Bratko ch. 15.1-15.5, 15.7, 16, 23.5- 23.6 | HW3due Mar 6 |
| Mar 11/13
No class this week! (Spring Break) |
some fresh air and sunshine | ||
| Mar 18/20
Mar. 19 is last day to drop course without academic penalty |
Reasoning under Uncertainty | ch. 10.1-10.3; optional: Bratko ch. 15.5-15.6; optional: www.dcs.qmul.ac.uk/~norman/SERENE_Help/start.htm (recommended topics in table of contents: Fundamentals of Probability Theory, Bayesian Belief Nets Tutorial) | |
| Mar 25/27
(AAAI Spring Symp. 3/23-26) |
Dr. Green's paper presented at AAAI 2003 Spring Symposium | no class 3/25 (Make-up will consist of Math Dept Seminar on my AI research on April 4); | |
| Apr 1/3 | Reasoning under Uncertainty (continued) | ch. 10.4 | HW4 due Apr 3; (optional) come to my Math Dept. Seminar on April 4, 3:30 pm 335 Bryan (some of this to be presented in August 2003 at CMNA) |
| Apr 8/10 | Planning | ch. 8 | April 8:Test
2 covers ch. 5-6, 10.1-10.3;
grad student presentations: C. Hecker 4/10 |
| Apr 15/17
(UNCG holiday Fri. 4/18) |
Planning (continued) | April 15 Guest speaker (Math Colloquium, 3:30 pm, 335 Bryan) on AI and Multimedia Learning Systems | grad student presentations:
K. Jirak 4/17 |
| Apr 22/24 | Learning | ch. 11.1-11.2 | grad student presentations:
T. Britt 4/22 Test 3 covers ch. 10.4, 8, 11 through p.408 (was Apr 24, now Apr 29) |
| Apr 29/May 1 |
|
HW5 was due Apr29, now due May 1! | |
| May 6
no class (Last day of classes for Friday classes only!) |
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| May 8-10, 12-14 final exams | no exam (exam time used for repeat demos) |
Other AI Languages and Software
Artificial Intelligence Publications (big bibliography)
General AI Conferences in North America
AI Applications Resources (other info on applications)
Send comments and requests about this web site to nlgreen@uncg.edu