Department of Computer Science
CS 580 Introduction to Artificial Intelligence
Meeting time: Tuesday
Meeting location: IN 132
For current information on this course see: http://lac.gmu.edu/cs580-fa04/cs580-tecuci-g.htm
Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office hours:
Tuesday and Thursday,
Office: ST-II, Room 421
Phone: 703 993 1722
E-mail: tecuci@gmu.edu
Course Description
Artificial Intelligence is the Science and Engineering domain which is concerned with the theory and practice of developing systems that exhibit the characteristics we associate with intelligence in human behavior such as reasoning, planning and problem solving, learning and adaptation, natural language processing, and perception. This course presents the basic principles and the major methods of Artificial Intelligence, preparing the students to build complex systems incorporating capabilities for intelligent processing of information. Covered topics include: heuristic search and game playing, knowledge representation and reasoning, problem solving and planning, learning and knowledge acquisition, knowledge engineering, expert systems and intelligent agents, Common LISP. The students will also learn about the Disciple agent development environment created in the Learning Agents Center of George Mason University.
Grading Policy
There will be several homework assignments, a mid-term exam and a final exam. It will be permissible, on an individual basis, to replace the assignments with a project. This option requires an early submission of a proposal.
The course grade will be determined as follows:
Assignments or project 33.3%
Mid-term exam 33.3%
Final exam 33.3%
Exam Dates
Mid-term exam:
Final exam:
Lateness Policy
Each assignment should be received by the day indicated as the deadline of the
assignment. Any delay will be penalized with 15%/day.
Objective cases of delay will be considered individually, and are not subject to
the above policy. An example of such a case is a longer business trip that
privents one to return the assignment in time. In such cases permission from
the instructor should be requested as soon as possible.
Honor Code Policy
Everyone has to do the assignments and the exams by
himself or herself. If it is determined that two assignments or exams are too
similar to have been done independently, then the grade will be split between
their authors. For example, in case of a 30p assignment each will receive 15p.
Required
Tecuci G., Lecture Notes in Artificial Intelligence, 2004, available online (see outline below).
Recommended
Russell S., and P. Norvig P., Artificial Intelligence: A Modern Approach, Prentice Hall, Second edition, ISBN: 0131038052, 2003.
Graham P., ANSI Common Lisp, Prentice Hall, ISBN: 0133708756, available on line.
Other Useful
Coppin B., Artificial Intelligence
Illuminated, Jones and
Dean T., Allen J., Aloimonos Y., Artificial Intelligence: Theory and Practice, The Benjamin/Cummings Pub. Comp.
Ginsberg M., Essentials of Artificial Intelligence, Morgan Kaufmann.
Luger G., Artificial Intelligence: Structures and Strategies for Complex Problem Solving,
Addison Wesley, 2002.
Negnevitsky M., Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, 2002.
Rich E., Knight K., Artificial Intelligence, McGraw-Hill.
Steele G.L., Common Lisp the Language, 2nd Edition.
Tecuci G., Building
Intelligent Agents: An Apprenticeship Multistrategy Learning Theory,
Methodology, Tool and Case Studies, Academic Press, 1998.
Wilensky R., Common LISPcraft, Norton & Company, 1989.
Winston P.H., Artificial Intelligence, Addison-Wesley.
Winston P.H., Horn B.K.P., LISP, Addison-Wesley.
G. Tecuci, Lecture Notes in
Artificial Intelligence, 2004
Overview
of Artificial Intelligence and Intelligent Agents
Common Lisp
Solving Problems by Searching (uninformed search; informed search; constraint satisfaction problems; adversarial search)
Knowledge Representation and Reasoning (logic; natural deduction; resolution; prolog; production systems; probabilistic reasoning; semantic networks and ontologies; planning; problem solving agents)
Machine Learning and Knowledge Acquisition (learning strategies: version spaces, decision trees, instance-based, explanation-based, analogical, multistrategy; problem solving and learning agents)