Instructor:
Dr. John R. Sullins
Office hours: MW
Office: 333 Meshel Hall
Phone: 742-1806
Email: john@cis.ysu.edu
Web site: http://cis.ysu.edu/~john/
Check the web site regularly, as assignments and announcements will be posted here.
Objectives:
Prerequisites:
The official prerequisites are CSIS 2617 and CSCI 3710. As far as content, the knowledge that you will need coming into this course is (1) the ability to write data-structure level programs in C/C++, and (2) a good understanding of propositional logic and graphs/trees.
Textbook:
Artificial Intelligence:
Structures and Strategies for Complex Problem Solving
(fourth edition), George F. Luger, Addison
Wesley.
Given the dynamic nature of this field, not all material introduced by this
course may be covered in the textbook. Where appropriate, I may provide my own
notes for some topics.
Grading:
|
Homework/Programming assignments |
25% |
|
|
Exam 1 |
12.5% |
Date TBA |
|
Exam 2 |
12.5% |
Date TBA |
|
Research project/paper |
25% |
Due final week of class |
|
Final Exam |
25% |
Wednesday, May 5, |
Last day to withdraw with a "W": Saturday, October 23
Homework Assignments:
The homework assignments may involve a combination of written problems and some simple programming (probably in C++) related to the application of core AI concepts. These will possibly include:
· Designing an evaluation heuristic for an AI game
· Simple applications in areas such as planning/learning/natural language understanding
Research Project/Paper:
You will be required to do either a research paper or a programming project (your choice) for this course:
· The research paper should be 20-25 pages long, and should survey the current state of some important or interesting area of Artificial Intelligence.
· The programming project is to be an implementation (in the language of your choice) of a program (such as a game) based on some AI-related algorithm.
In either case, you will be asked to give a short presentation on your paper or project during the last week of class. More details will be available as the semester progresses.
Tentative Course Outline:
|
WEEK |
TOPICS |
TEXTBOOK |
|
8/23 |
Introduction to AI, Propositional and Predicate logic |
1, 2 |
|
8/30 |
State space representation of problems, Heuristic search |
3, 4 |
|
9/6 |
Game playing as search (no class Monday) |
4 |
|
9/13 |
Deductive systems, PROLOG |
5, 14 |
|
9/20 |
Advanced knowledge representation techniques |
6, 7 |
|
9/27 |
Planning |
7 |
|
10/4 |
Reasoning and Uncertainty |
8 |
|
10/11 |
Probabilistic/Fuzzy reasoning |
8 |
|
10/18 |
Syntactic natural language understanding |
13 |
|
10/25 |
Semantic and probabilistic natural language understanding |
13 |
|
11/1 |
Knowledge-based learning |
9 |
|
11/8 |
Neural network learning |
10 |
|
11/15 |
Genetic learning |
11 |
|
11/22 |
Advanced Topics (no class Friday) |
|
|
11/29 |
Project/Research paper presentations |
|
|
12/6 |
Final Exam (Wednesday |
|