CPSC 420-500 Artificial Intelligence:
Fall 2008


NEWS: 12/5/08, 03:49AM (Fri)
Read-Only Bulletin Board.: 8/25/08, 10:05PM (Mon)

Page last modified: 9/1/08, 12:21AM Monday.

General Information Resources Weekly Schedule Credits Lecture Notes Example Code Read-Only Board

I. General Information


Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 979-845-5466
Office hours: Tue/Thu 2:00pm–3:00pm.


Lei He
Email: arcadia_hlyy(a)neo.tamu.edu
Office: HRBB 526
Phone: 739-8829
Office hours: Wed/Fri 4:00pm–5:30pm


CPSC 311


Tue/Thu 12:45pm-2:00pm, HRBB 113


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.).


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. You may use a different language but example code will only be made available in Lisp.
  2. CMU Common Lisp:

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. Special topics


  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'.

If you are absent without any prior notification to the instructor, your class participation score will be set to 0% at the very first occurrence, except for excuses allowed by the university rules (medical, etc.).

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:

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.

II. Resources

  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)

III. Weekly Schedule and Class Notes

Notices and Dues
1 8/26 Introduction Chapter 1
1.1 and 1.2
  First day of class slide01.pdf
1 8/28 Introduction, Lisp Chapter 26
26.1 and 26.2
2 9/2 Lisp, Symbolic Differentiation Lisp quick ref Program 1 announced   slide02.pdf
2 9/4 Uninformed Search (BFS,DFS,DLS,IDS) Chapter 3.1-3.5 (3.6,3.7 optional)     slide03.pdf
3 9/9 Informed Search (BestFS,Greedy,A*) Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3)     slide03.pdf
3 9/11 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4   Program 1 due (11:59pm): extended to 9/16 slide03.pdf
4 9/16 Game playing
Min-Max, Alpha-Beta
Chapter 5 (optional) and 6.1-6.8 (old 5) Program 2 announced   slide03.pdf
4 9/18 Game playing
Chapter 5 (optional) and 6.1-6.8 (old 5)   slide03.pdf
5 9/23 Game playing wrap up; Propositional Logic Chapter 7.1, 7.3, 7.5, 7.6 (old 6)     slide03.pdf
5 9/25 Theorem proving Chapter 9 (old 10) Homework 1 announced   slide04.pdf
6 9/30 FOL; Theorem proving
for FOL
Chapter 8 (old 7); Chapter 9 (old 10)   Program 2 due (11:59pm) slide04.pdf
6 10/2 Inference
for FOL
Chapter 9   Homework 1 due, in class slide04.pdf
7 10/7 Midterm Exam In class      
7 10/9 Uncertainty Chapter 13 (old 14)   Program 2, extended deadline 11:59pm slide05.pdf
8 10/14 Uncertainty Chapter 13 (old 14)     slide05.pdf
8 10/16 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)     slide05.pdf
9 10/21 Learning Chapter 14 (old 15)     slide06.pdf
9 10/23 Learning Chapter 18     slide06.pdf
10 10/28 Learning Chapter 18     slide06.pdf
10 10/30 Learning Chapter 18 Program 3 and Paper commentary announced;   slide06.pdf
11 11/4 Learning Chapter 20 (old 19) Homework 2 announced (Monday)   slide06.pdf
11 11/6 Learning Chapter 20 (old 19)     slide06.pdf
12 11/11 Advanced topic Autonomous semantics   Homework 2 due, in class slide06.pdf
12 11/13 Advanced topic Neuroevolution     slide09.pdf
13 11/18 Final Exam     Society for Neuroscience meeting  
13 11/20 Advanced topic Binary spatter code; Internal state predictability     slide10.pdf
14 11/25 Planning, Natural language processing       slide12.pdf
14 11/27 No class (Thanksgiving)        
15 12/2 Natural language processing; Course wrap-up TBA   Paper commentary due in class, 12/2; Program 3 due by 12/4 11:59pm  

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).

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