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General Overview:

This course provides an overview and introduction to the field of Artificial Intelligence. Notions of rational behavior and intelligent agents will be discussed. Major subareas will be covered. The emphasis will be on understanding the fundamental concepts, as well as being able to practically apply the corresponding approaches in solving practical problems and developing useful software applications. Program illustrations in traditional languages such as C and Java will be used, and the logic programming language Prolog will be introduced.

Course Topics:

History of artificial intelligence, philosophical questions about nature of intelligence, ethical issues in artificial intelligence, nature of knowledge and knowledge based systems, issues of ordering of information, modeling the world. Artificial intelligence design considerations, challenges and issues in artificial intelligence system design. Introduction to LISP programming. Blind and heuristic search concepts and techniques. Game playing and constraint propagation. Machine learning for classification and pattern recognition. Knowledge representation. Logical Reasoning systems. Uncertainty and Planning. Natural Language Processing. Vision: vision level and segmentation, constraint propagation and matching.

Course Objectives:

The objectives of this course are to equip students with the required knowledge in artificial intelligence and its applications and be able appreciate the immense contribution from the field of AI research. The course will offer the foundation required in developing software programs that mimic rational human-like behaviour. A student who completes the course successfully will be able to demonstrate among other things:

• Understanding of the notions of rational behavior and intelligent agents.
• Knowledge of methods of blind as well as informed search and ability to practically apply the corresponding technıques.
• General understanding of major concepts and approaches in knowledge representation, planning, learning, robotics and other AI areas.
• Development of programming skills for AI applications.
• Exposure to the programming of search algorithms in C++, Java, C# and Python to solve routing and navigation problems, Unsupervised learning, Neural Networks, Fuzzy systems and logic programming with Prolog.

Reference books:

1. Stuart Russel and Peter Norvig, Artificial Intelligence: A Modern Approach, 3^rd Ed., Prentice-Hall, 2003
2. AI Algorithms, Data Structures, and Idioms in Prolog, Lisp and Java, G. Luger and W. Stubblefield, Addison Wesley, 2009
3. Eric Bonabeau, Marco Dorigo, Guy Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999
4. Forster, Dion (2006). "Self validating consciousness in strong artificial intelligence: An African theological contribution". Pretoria: University of South Africa.
5. Gladwell, Malcolm (2005). Blink. New York: Little, Brown and Company. ISBN 0-316-17232-4.
6. Haugeland, John (1985). Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press. ISBN 0-262-08153-9.
7. Hawkins, Jeff; Blakeslee, Sandra (2004). On Intelligence. New York, NY: Owl Books. ISBN 0-8050-7853-3.
8. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6^th ed. G. Luger, Addison Wesley, 2009
9. Artificial Intelligence: A Systems Approach. M. Tim Jones, Infinity Science Press, 2008