# CPSC 625-600 Artificial Intelligence:Fall 2008

## Syllabus

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

## I. General Information

### Instructor:

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

None

### Prerequisite/Restrictions:

CPSC 311 or equivalent

### Lectures:

Tue/Thu 11:10am-12:25pm, 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. 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: 30% (midterm: 15%, final: 15%)
2. Homeworks: 15% (about 3, 5% each)
3. Programming Assignments: 24% (about 2, 12% each)
4. Term project and report: 31%
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.).

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.

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.

## 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
8. An interesting popular view of AI
9. Chess playing program (with neat visualization)

## III. Weekly Schedule and Class Notes

• 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 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.
 Week Date Topic Reading Assignments Notices and Dues Notes 1 8/26 Introduction Chapter 11.1 and 1.2 First day of class slide01.pdf 1 8/28 Introduction, Lisp Chapter 2626.1 and 26.2 slide01.pdfslide02.pdf 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 playingMin-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.pdfslide04.pdf 5 9/25 Theorem proving Chapter 9 (old 10) Homework 1 announced slide04.pdf 6 9/30 FOL; Theorem provingfor FOL Chapter 8 (old 7); Chapter 9 (old 10) Program 2 due (11:59pm) slide04.pdf 6 10/2 Inferencefor 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) 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 Advanced topic Autonomous semantics slide07.pdf 11 11/4 Advanced topic Evolution of agency Homework 2 announced (Monday) slide08.pdf 11 11/6 Learning Chapter 18 slide06.pdf 12 11/11 Learning Chapter 20 (old 19) Homework 2 due, in class slide06.pdfslide09.pdf 12 11/13 Advanced topic, Final exam review Binary spatter code (Kanerva) slide10.pdf 13 11/18 Final Exam Society for Neuroscience meeting 13 11/20 Project presentation 14 11/25 Project presentation 14 11/27 No class (Thanksgiving) 15 12/2 Project presentation

## 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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