The course aims to impart the following knowledge and skills:

1. An introduction to the principles and methods used in artificial intelligence programs.
2. To familiarize the student with several problem solving techniques , mainly search and logic, in AI.
3. Knowledge representation techniques
4. To familiarize the student with languages used in AI problem solving.
5. To present alternate ways of representing knowledge and explore the consequences of the various representations.
6. Up-to-date introduction to the theory and practice of AI

Students will implement many of the algorithms we cover in programming assignments. The implementation language for these assignments will be LISP which will be taught in the first two weeks of the course.

Introduction (4 hours)
History and definition of Artificial Intelligence.; AI problems and problem spaces : Game Playing; Planning; Understanding; Natural Language Processing; Parallel and Distributed AI; Learning; Connectionist Model; Common Sense; Expert Systems; Perception and Action; Fuzzy Logic; Neural Network; Intelligence Agents.

Problem Solving (4 hours)
Solving Problems by Searching; Informed Search Methods; Game Playing.

Knowledge and Reasoning (4 hours)
Agents that Reason Logically; First Order Logic; Building a Knowledge Base; Inference in First-Order Logic; Logical Reasoning Systems. (Languages for AI Problem Solving: A. PROLOG ; B. LISP )

Acting Locally (4 hours)
Planning; Practical Planning; Planning and Acting.

Uncertain Knowledge And Reasoning (4 hours)
Uncertainty; Probabilistic Reasoning Systems; Making Simple Decisions.

Learning (4 hours)
Learning from Observations; Learning from Neural Network; Reinforcement learning; Knowledge in Learning.

Communicating, Perceiving And Acting (2 hours)
Agents that communicate; Practical Communication in English; Perception; Robotics.

Future Perspectives (4 hours)
Philosophical Foundations; AI: Present and Future. (Evolutionary Computing: hard problems and approximate methods; stochastic iterative search; evolutionary search; classifier systems; applications.)

Tutorials/Assignments:

1. Basic concepts
2. Searching by depth-first search and breadth-first search techniques
3. Reasoning with LISP and First-Order Predicate Logic
4. Machine learning (Clustering and Game playing)

References:

• Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach", Prentice Hall , 1995.
• S. Russell, "Artificial Intelligence: A Modern Approach", Prentice Hall, 1995
• Elaine Rich et al, "Artificial Intelligence", McGraw Hill,1991.
• Dan W. Patterson, Introduction to "Artificial Intelligence and Expert Systems", Prentice Hall, 1990.
• E.Turban, "Expert Systems and Applied Artificial Intelligence", MacMillan, 1992.
• T. Dean, J. F. Allen & Y. Aloimonos, Thomas Dean, and James Allen, and Yiannis Aloimonos, "Artificial Intelligence: Theory and Practice", Benjamin Cummings, 1995.
• P.H. Winston, "Artificial Intelligence", Addison-Wesley, Reading, Massachusetts, Third Edition, 1992.
• S. Tanimoto, "The Elements of Artificial Intelligence Using Common LISP", Computer Science Press, Rockville, Maryland, 1995.
• G.F. Luger, and W.A. Stubblefield, "Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 3rd ed." , Addison-Wesley Publishing Company, 1997.
• Patrick Winston and Berthold Horn, "LISP", Addison-Wesley, 1984.
• P. Graham, "ANSI Common Lisp", Prentice Hall, 1995.
• Norvig, "Paradigms of Artificial Intelligence Programming - Case Studies Using Common Lisp", Morgan Kaufmann, San Mateo, California. 1992.
• Chin-Teng Lin and C.S. George Lee, Neural Fuzzy Systems: A neuro-Fuzzy Synergism to Intelligent Systems.

* Note: The hours in bracket indicate the lecture hours.