OLD WEB PAGE for CPSC 420-500 Artificial Intelligence:
Fall 2006


NEWS: 12/13/06, 03:33PM (Wed)
  • *** Final letter grades posted ***
  • [Graduating seniors]: If you have any issues with the grade, come see me before 5pm 12/13. I'll submit the grade by 5pm, since I'm leaving on a trip tomorrow morning. If you are unable to come, call me at 845-5466.
  • [All the rest]: If you have any issues, send me an email ASAP, and come see me between 10am-11am on Monday 12/18. You may review your final exam then.
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Read-Only Bulletin Board.: 8/24/06, 08:40AM (Thu)

Page last modified: 9/19/06, 11:42AM Tuesday.

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 11:00am–12:30pm.


Yiliang Xu
Email: ylxu(a)cs.tamu.edu
Office: Richardson 911 (ask for the TA room)
Phone: 979-845-7143
Office hours: MWF 10-11am


CPSC 311


Tue/Thu 12:45pm-2:00pm, ZACH 105B


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. Example code will only be made available in Lisp, and in general other languages will not be permitted.
  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 unforseen emergencies.

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 8/29 Introduction Chapter 1
1.1 and 1.2
  First day of class slide01.pdf
1 8/31 Introduction Chapter 26
26.1 and 26.2
  Unix basics (DIY) slide01.pdf
2 9/5 Lisp, Symbolic Differentiation Lisp quick ref     slide02.pdf
2 9/7 Uninformed Search (BFS,DFS,DLS,IDS) Chapter 3.1-3.5 (3.6,3.7 optional) Program 1 announced   slide03.pdf
3 9/12 Informed Search (BestFS,Greedy,A*) Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3)     slide03.pdf
3 9/14 IDA*,Heuristic Search,
Simulated Annealing, etc.
Chapter 4     slide03.pdf
4 9/19 Game playing
Min-Max, Alpha-Beta
Chapter 5 (optional) and 6.1-6.8 (old 5) Program 2 announced Program 1 due: 9/18 (Monday) 11:59pm slide03.pdf
4 9/21 Game playing
Chapter 5 (optional) and 6.1-6.8 (old 5)   slide03.pdf
5 9/26 Game playing wrap up; Propositional Logic Chapter 7.1, 7.3, 7.5, 7.6 (old 6)     slide03.pdf
5 9/28 Theorem proving Chapter 9 (old 10) Homework 1 announced slide04.pdf
6 10/3 FOL; Theorem proving
for FOL
Chapter 8 (old 7); Chapter 9 (old 10)   slide04.pdf
6 10/5 Inference
for FOL
Chapter 9   Program 2 part 1 (uninformed search) due 11:59pm slide04.pdf
7 10/10 Uncertainty Chapter 13 (old 14)   Homework 1 due (in class) slide05.pdf
7 10/12 Midterm Exam   In class exam. 10/10:Midsemester grades due  
8 10/17 No class     Society for Neuroscience conference (make-up TBA)  
8 10/19 Uncertainty Chapter 13 (old 14)     slide05.pdf
9 10/24 Uncertainty Chapter 13 (old 14), Chapter 14 (old 15)   Program 2 part 2 (informed search) due 10/23 Monday 11:59pm slide05.pdf
9 10/26 Learning Chapter 14 (old 15)     slide06.pdf
10 10/31 Learning Chapter 18   slide06.pdf
10 11/2 Learning Chapter 18     slide06.pdf
11 11/7 Learning   Homework 2 announced;
Paper commentary assignment announced
11 11/9 Learning Chapter 20 (old 19) Program 3 announced   slide06.pdf
12 11/14 Learning     Homework 2 due (in class) slide06.pdf
12 11/16 Learning   Homework 3 TBA   slide06.pdf
13 11/21 Advanced topics Thalamus and analogy     slide07.pdf
13 11/23 No class     Thanksgiving holiday  
14 11/28 Advanced topics Autonomous semantics   slide08.pdf
14 11/30 Natural language processing     Paper commentary due (in class);
Course Evaluation.
15 12/5 Advanced topics; Course wrap-up Analogy   Homework 3 due;
Last day of class
15 12/7   Semester ended: no class   Program 3 due 12/7 11:59pm  
 12/13Final Exam  8:00--10:00am, ZACH 105B

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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