| Course Description | Course Calendar | Grading | Policies | Resources | Project |
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:
Optional Textbooks:
1. Prolog
Programming for Artificial Intelligence,
by Ivan Bratko, 3rd ed. Addison Wesley,
2001.[code
downloads]
2. Data Mining: Practical Machine Learning Tools with Java Implementations,
by I. Witting & E. Frank, Morgan Kaufmann, 2000. [code
downloads]
Handouts: You are also responsible for reading supplementary materials handed out in class or placed on-line for the class.
| Tu/Th | Topic this week | Textbook
Reading |
Due Dates/Tests |
Other |
| Jan 11/13 | Introduction to AI, Logic Programming (LP) | ch. 1 | (optional) Bratko ch. 1-3, 5-7, 8.4, 9.1; or other Prolog book | |
| Jan 18/20 | ||||
| Jan 25/27 | Logical Foundations (LF) | ch. 2-3 | ||
| Feb 1/3 | HW 1 due Feb 1 | |||
| Feb 8/10 | Search | ch. 4 | Test 1 (ch. 1-3, etc.) Feb 10 | |
| Feb 15/17 | HW 2 due Feb 15 | |||
| Feb 22/24 | Knowledge Representation | ch. 5 | (optional) Coppin ch. 9 | |
| Mar 1/3 | Knowledge Engineering | ch. 6 | HW 3 due Mar 3 (late penalty waived until Mar 15 in class) | AI Colloquium, 3:30 Mar 3, Bryan 335 |
| Mar 8/10 (spring break) | no class | Project proposal due after spring break | ||
| Mar 15/17 | Assumption-based reasoning | ch. 9 | AI Colloquium, 3:30 Mar 17, Bryan 335 | |
| Mar 22/24 | no class Mar 22 | Test 2 (ch. 4-6) Mar. 24 | ||
| Mar 29/31 | Reasoning under uncertainty | ch. 10 | ||
| Apr 5/7 | ||||
| Apr 12/14 | Planning | ch. 8 | HW 4 due Apr 14 | |
| Apr 19/21 | AI/planning in games and interactive narrative (Guest lecture) | Final test (ch. 8-10) Apr 21 | ||
| Apr 26/28 | no lecture (demo week) | Final project (with demo) due Apr 26, cont. demo Apr 28 | ||
| May 3 no class (Friday classes meet today) |
| Description | Points (Undergraduate students) | Points (Graduate students) |
| Test 1 | 25 | 25 |
| Test 2 | 25 | 25 |
| Final test | 15 | 15 |
| HW 1 | 5 | required but no points |
| HW 2 | 5 | 5 |
| HW 3 | 5 | 5 |
| HW 4 | 5 | 5 |
| HW 5 (Project) | 15 | 15 |
| Extra presentation | not required | 5 |
Attendance is required. You may be dropped from the course for missing more than five 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.
Samples of student work
(assignments and tests) may be shown to reviewers for departmental accreditation.
Prolog