The Volgenau School of Information Technology and Engineering
Department of Computer Science
CS 580 Introduction to Artificial Intelligence
Meeting time: Tuesday 4:30 pm – 7:10 pm
Meeting location: The Engineering Building 4457
Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office hours: Tuesday
3:30 pm – 4:20 pm
Office: The Engineering Building 4613
Phone: 703 993 1722
E-mail: tecuci at gmu dot edu
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 and
Prolog. The students will also learn about and use the Disciple agent
development environment created in the Learning
Agents Center of
This course will use Elluminate, a distance learning software package (see instructions at http://volgenau.gmu.edu/DE/docs/Elluminate%20student%20instructions.pdf).
There will be several homework assignments, a mid-term exam and a final exam.
The course grade will be determined as follows:
Assignments or project 33.3%
Mid-term exam 33.3%
Final exam 33.3%
Mid-term exam: 10/27/2009
Final exam: 12/15/2009
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 before the deadline.
Honor Code Policy
You are expected to abide by the University's honor code. Any collaboration between students on assignments or exams is unacceptable. If it is determined that two assignments or exams have not been done independently, then the grade will be split between their authors. For example, in case of a 30p assignment each will receive 15p. We reserve the right to use MOSS to detect plagiarism in the programming assignments.
Tecuci G., Lecture Notes in Artificial Intelligence, 2009 (available online).
Russell S., and P. Norvig P., Artificial Intelligence: A Modern Approach, Prentice Hall, Second edition, ISBN: 0-13-790395-2, 2003.
Graham P., ANSI Common Lisp, Prentice Hall, ISBN: 0133708756, available on line.
Tecuci G., Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies, Academic Press, 1998.
Giarratano J. and Riley G., Expert Systems: Principles and Programming,
Third Edition, PWS Publishing Company,
Wilensky R., Common LISPcraft, Norton & Company, 1989.
Winston P.H., Artificial Intelligence, Addison-Wesley.
Winston P.H., Horn B.K.P., LISP, Addison-Wesley.
Luger G., Artificial Intelligence: Structures and Strategies for Complex Problem Solving,
Addison Wesley, 2009.
Jones T.M., Artificial Intelligence: A Systems Approach,
Jones and Bartlett Publishers, 2009.
Rich E., Knight K., Artificial Intelligence, McGraw-Hill.
Bratko I., PROLOG Programming for Artificial Intelligence, Addison Wesley.
Coppin B., Artificial Intelligence Illuminated, Jones and Bartlett publishers, 2004.
Dean T., Allen J., Aloimonos Y., Artificial Intelligence: Theory and Practice, The Benjamin/Cummings Pub. Comp.
Ginsberg M., Essentials of Artificial Intelligence, Morgan Kaufmann.
Negnevitsky M., Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, 2002.
Steele G.L., Common Lisp the Language, 2nd Edition.
G. Tecuci, Lecture Notes in Artificial Intelligence, 2009
Overview of Artificial Intelligence and Intelligent Agents
Solving Problems by Searching (state-space and problem reduction representations; uninformed search; informed search; constraint satisfaction problems; adversarial search)
Knowledge Representation and Reasoning (logic; natural deduction; resolution; prolog; production systems; probabilistic reasoning; semantic web and ontologies; planning; problem solving agents)
Machine Learning and Knowledge Acquisition (learning strategies: version spaces, decision trees, instance-based, case-based, explanation-based, analogical, multistrategy; problem solving and learning agents)
1. Please include CS580 in the subject of any message you are emailing to Dr. Tecuci.
2. Please try to limit the size of the files you are emailing.
3. If you are a section 2 (Internet) student, you can either mail the assignments to Dr. Tecuci by the indicated deadline, or scan and email them both to Dr. Tecuci and to the Teaching Assistant (again making sure that the files are not too large).
4. The mail address of Dr. Tecuci is:
Prof. Gheorghe Tecuci