CPSC 420-500 Artificial Intelligence:
Fall 2007

Syllabus

I. General Information

Instructor: Dr. Yoonsuck Choe Email: choe(a)tamu.edu Office: HRBB 322B Phone: 979-845-5466 Office hours: MWF 1:40pm–2:40pm.

TA: Huei-Fang Yang Email: hfyang(a)cs.tamu.edu Office: HRBB 322A Phone: 979-845-5481 Office hours: T 4:30pm–6pm, Th 1:00pm–2:30pm

Peer Teacher: Daniel Eng Email: edaniel1984(a)gmail.com Office: HRBB 339 Phone: Office hours: MW 4:30pm–6:00pm

Prerequisite/Restrictions:

CPSC 311

Lectures:

MWF 10:20am–11:10am HRBB 113

Goals:

To understand the problems in AI and to learn how to solve them:

1. traditional methods in AI (search, pattern matching, logical inference, theorem proving, etc.).
2. modern approaches in AI (learning, probabilistic approaches, etc.).

Textbook:

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA, hereafter), 2nd Edition, Prentice Hall, New Jersey, 2003.
ISBN 0-13-790395-2
Book Homepage
* The first edition may be okay if that's what you have.

Computer Accounts and Usage:

1. Computer accounts: if you do not have a unix account, ask for one on the CS web page. We will be using the CMU Common Lisp as our main language. Example code will only be made available in Lisp.
2. CMU Common Lisp:
   • Carnegie Mellon U. Common Lisp homepage
   • On all SunOS systems in the department (sun.cs.tamu.edu, interactive.cs.tamu.edu, compute.cs.tamu.edu, etc.), the program is installed in /opt/apps/cmucl/bin/lisp.
   • See the Read-only Board for a brief example.

Topics to be covered:

See the Weekly Schedule section for more details.

1. Introduction
2. LISP
3. Search
4. Game playing, alpha-beta pruning
5. Propositional Logic, first-order logic, theorem proving
6. Uncertainty, probabilistic approaches
7. Learning
8. Planning
9. Special topics

Grading:

1. Exams: 40% (midterm: 20%, final: 20%)
2. Homeworks: 15% (about 3, 5% each)
3. Programming Assignments: 36% (about 3, 12% each)
4. Paper commentary: 4% (1 page, single-spaced)
5. Class participation: 5%

Grading will be on the absolute scale. The cutoff for an `A' will be 90% of total score, 80% for a `B', 70% for a `C', 60% for a `D', and below 60% for an 'F'.

Class participation will be based on attendance (important) and other factors.

Academic Integrity Statement:

AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.

Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.

For additional information please visit: http://www.tamu.edu/aggiehonor/

Local Course Policy:

• All work should be done individually and on your own unless otherwise allowed by the instructor.
• Discussion is only allowed immediately before, during, or immediately after the class, or during the instructor's office hours.
• If you find solutions to homeworks or programming assignments on the web (or in a book, etc.), you may (or may not) use it. Please check with the instructor.

Students with Disabilities:

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life, Services for Students with Disabilities, in Cain Hall or call 845-1637.

1. LISP quick reference
2. CMU Common Lisp (This one will be used in the class.)
3. GCL manual (very in-depth and technical).
4. GNU Common Lisp
5. Lisp resources
6. My general resources page
7. 625/689 Reading List
8. An interesting popular view of AI
9. Chess playing program (with neat visualization)

• Lecture notes (in PDF format): all notes will be uploaded in this directory.
• It is your responsibility to download, print, and bring the notes to the class. Notes will be available 24 hours before each class.
• See the 2007 Fall TAMU Calendar for breaks, etc.
• When reading the chapters, you do not have to memorize everything. A separate list of terms you need to know will be handed out prior to each exam.
• All reading material below refers to the AIMA book 2nd edition. The (old XX) tags next in the Reading field are the corresponding chapters in the old AIMA book (1st edition). To see how the 1st and the 2nd edition chapters correspond, see the "AIMA 1st and 2nd edition chapter map".
• More detail will be available as we go along.

IV. Credits

Many ideas and example codes were borrowed from Gordon Novak's AI Course and Risto Miikkulainen's AI Course at the University of Texas at Austin (Course number CS381K).