CSC345   Artificial Intelligence     Fall 2006

Links:   Homeworks and programming assignments   

Instructor and Office Hours
Lonnie Fairchild
Office: Redcay 147     (Enter through Computer Science Dept.)
Phone: 564-2783,         Computer Science Office: 564-2788
Email: lonnie.fairchild@plattsburgh.edu,         
Office hours:     Mon.  4:20 - 6  in Hawkins lab (053B)          Tues.  11-12 Redcay 147                
                         Wed.  11-12    Redcay 147                          Thurs.  11-12 Redcay 147
These times will not be convenient for everyone. Students are welcome (and encouraged) to make appointments for other times.

Course Description   
This course is concerned with theories and techniques used in getting computers to perform  tasks which seem to require  “intelligence”.  These have sometimes been described as tasks at which "people do better" (E. Rich).  Typically they require huge amounts of knowledge, so we will focus on ways of representing this knowledge and associated programming techniques.  We will note areas where AI has been more (and less) successful and future possibilities.

Prerequisite: CSC 223 (Note that CSC217 will be assumed since it was a prerequisite for CSC223)

Required Text     
Alison Cawsey, The Essence of Artificial Intelligence, Pearson/Prentice Hall, 1998
This book is small and other readings will be assigned to supplement it.

Other useful resources
Textbooks (available in my office):
    Russell, Stuart & Norvig,Peter, Artificial intelligence: A Modern Approach, 2nd edition, 
             Prentice Hall, 2005
    Rich, Elaine & Knight,Kevin, Artificial Intelligence, 2nd edition, McGraw Hill, 1991
    Luger, George, Artificial Intelligence:Structures and Strategies for
         Complex Problem Solving, 5th edition, Benjamin/Cummings, 2005
AAAI (American Association of Artificial intelligence) website  This is a rich site with many good summary articles, lists of recent news articles, bibliographical resources, etc.  It's worth some careful exploration.
AI on the Web (maintained by Russell & Norvig)

Grading The final course grade will be computed as follows:
            Quizzes                                    10 %
            Take-home exams [2]               20 %
            Written assignments  [weekly]    20 %
            Programming assignments [4]     40 %    
            Class participation                     10 % (more if classes are missed)

Quizzes A short written quiz will be given on most Thursdays. Questions will come from the assigned readings and exercises, material discussed in the previous classes, and recently completed programming assignments.  Occasionally, take-home quizzes will be given instead.

Class participation  and attendance policy      This course depends heavily on class discussion and it is essential that all students participate actively in the class. (Note: This does not require that you speak constantly. It does require that you come to class prepared, with a willingness to listen carefully to what others say and a willingness to share the thinking that you have put into your assignments.) Hence, students will be expected to attend all classes. Students who must miss a class should notify (email or phone) the instructor before the class. The instructor is glad to help anyone who needs to make up work due to unavoidable emergencies (illness, etc.).

Group work      Working in pairs or small groups will be useful on some of the programming assignments.  Collaboration can make the work more interesting, and provide valuable opportunities to learn from other students and refine your thinking. When submitting assignments in which collaboration took place, the names and contributions of all collaborators must be clearly listed.  If collaboration is appropriate on an assignment, more detail will be provided when the assignment is handed out.

Academic honesty       All work submitted as your own (or as your group's) must be your own. A student who is found guilty of cheating on an exam or assignment risks failing the course.

Class format    The subject matter requires both mastery of technical material and reflection.  Class time will be divided about equally between presentations of technical material and discussion of study questions and exercises.  Homework exercises (generally done with pencil and paper) will be assigned regularly.   Students may occasionally be asked to make short, informal, oral presentations (with advance notice) on their answers to homework problems.

Tentative schedule: This is approximate and does not describe the exact pacing of the course.  More details and dates (as well as additional readings) will be added on a regular basis.

1                       Introduction; historical background;               Chapter 1
2 - 4                 Knowledge representation and inference        Chapter 2
5, 6                  Search, game-playing                                     Chapter 4
7 - 9                 Expert systems                                              Chapter 3
10 - 11             Natural language processing                          Chapter 5
12 -14              Machine learning                                           Chapter 7
15                     To be used where needed

09/05/06