Computer Science 470 – Artificial Intelligence

Course Syllabus

Fall 2000

Professor:

Dr. Bill Manaris

Office:

Room: 210 J.C. Long Building
Phone: (95)3-8159

E-mail: manaris@cs.cofc.edu

Office Hours:

MWF 1:00 – 3:00 p.m., and by appointment.

Course
Description:

A course introducing the principles of artificial intelligence, especially basic techniques for problem solving and knowledge representation.  Among topics covered are search strategies and heuristics; resolution, production systems, rule-based systems, expert systems; and natural language processing, semantic nets and frames. Artificial intelligence programming techniques will also be introduced, particularly Lisp or Prolog. 

Prereq: CSCI 325, 330 and MATH 307.

 

Topics:

Definition of Artificial Intelligence, philosophical issues, overview of LISP, intelligent agents, problems and search, informed search methods; game playing, knowledge and reasoning (first-order logic, knowledge representation, inference), uncertainty, probabilistic reasoning systems, natural language processing, expert systems, connectionist models, case studies, social/ethical/professional issues.

 

Textbooks:

G. F. Luger & W. A. Stubblefield (1998).  Artificial Intelligence – Structures and Strategies for Complex Problem Solving, 3rd ed., Addison-Wesley.

 

P. H. Winston and B. K. P. Horn (1993). LISP, 3rd ed., Addison-Wesley.

 

References

References will be made available via handouts and/or the class webpage.

 

Goals:

·         To gain fundamental knowledge on AI, including its definition(s), history, and main philosophical issues.

·         To understand knowledge representation schemes and related issues.

·         To understand the fundamental differences between symbolic, statistical, and connectionist approaches.

·         To gain experience in programming with a programming language used in AI applications.

·         To develop working knowledge, in terms of understanding the theory and being able to design/implement working applications, of one AI area, such as natural language processing or expert systems.

·         To develop an understanding of AI’s impact on today's society.

 

Grading:

Scale: A: 90–100 B: 80–89 C: 70–79 F: < 70.  The grades B+, C+ and D may be given at the professor’s discretion.

Final Grade Computation: Assignments (4-8) 30%, Tests (2) 40%, Comprehensive Final Exam 25%, and Class Participation 5%.

 

Class Policies:

·         Attendance at tests is mandatory.

·         To receive a passing grade for the course, you must average a passing grade (70% or higher) on each of the following: assignments, tests, and final exam.

·         You may write your programs alone or in partnership with classmates.  Working with others excludes giving or getting partial or finished solutions.  Any discussion of the assignment is fine, but you must type in and run your own program.  If you have any questions as to what is acceptable, please discuss it with me.  

·         If you work with others on a program, it is an honor offence not to report this accurately.   Be sure to describe completely the nature of any collaboration in the opening comments of your submitted solution. Use the "Certification of Authenticity" format given in the assignment.  There is no penalty for legal collaboration.

·         Do not submit programs with syntax errors.  They are not eligible for partial credit.

·         You have four “late” days for the whole semester to use when submitting your assignments.  Once you use up these days, no late assignments will be accepted. However, partial solutions submitted on time will be graded. If at the end of the semester you still have ALL 4 “late” days unused, you will earn a bonus of 2.5 points towards the final grade.