Syllabus

Course:         CS 480  Introduction to Artificial Intelligence

Semester:       Fall 2009

Instructor:     K. De Jong

Office:         Rm 4452 Engineering Bldg.

Phone:          993-1553

Email:          kdejong@gmu.edu

Class Hours:    W       16:30 - 19:10   B218 Robinson Hall

Office Hours:   W       15:00 - 16:00   

Course Text:    Artificial Intelligence: A Modern Approach (2nd edition)

                     by Russell & Norvig, Prentice Hall Publishing

Supplementary texts:

                ANSI Common Lisp, Graham, Prentice-Hall

                Common LISPcraft, R. Wilensky, Norton Publishing

                Common Lisp - The Language, G. Steele, Digital Press

                Prolog Programming, Clocksin & Mellish, Springer-Verlag

                Artificial Intelligence Through Prolog, N. Rowe, Prentice Hall

                Symbolic Computing with Lisp & Prolog, Mueller & Page, Wiley Publ.

Prequisites:    A working knowledge of computer systems and several

                programming languages is required.  The material covered

                in CS 312 and Math 305 as well as general computer science

                maturity is assumed and used throughout the course.

Content:        The basic principles of representation, heuristic search,

                and control will be presented in the context of specific

                AI areas such as problem solving, vision, natural language,

                and expert systems.  The Lisp programming language will be

                used as the primary language for homework assignments.

                Some exposure to logic programming via Prolog is planned.

Outcomes:       Students will obtain a basic understanding of uninformed and

                heuristic search techniques, of basic logic and probabilistic

                reasoning techniques, and of basic machine learning techniques.

                Students will obtain the ability to implement basic AI methods

                in Lisp or Prolog, and will have the ability to identify and

                apply basic AI methods to a given problem.

Exams:          There will be a midterm and final exam.

Homework:       There will be 4-6 programming assignments which will

                include written summaries.  A class project will be required.

Grading:        The course grade will be determined approximately as follows:

                        homework:       40%

                        project:        10%

                        midterm:        20%

                        final:          30%