Artificial Intelligence

Prerequisites:

Algorithms Design

Text Books:

[1] S. Russell and P. Norvig, "Artificial Intelligence; A Modern Approach" , 1995, 2003.
[2] E. Rich and K. Knight, "Artificial Intelligence" , 1992.

Reference Books:

[3] D. Poole, A. Mackworth and R. Goebel, "Computational Intelligence: A Logical Approach" , 1998.
[4] N. J. Nilsson, "Principle of Artificial Intelligence" , 1993.
[5] M. Watson, "Practical Artificial Intelligence Programming in Java" , 2005.
[6] A. Konar, "Artificial Intelligence and Soft Computing" , 2000.
[7] B. Coppin, "Artificial Intelligence Illuminated" , 2004.
[8] M. T. Jones "Artificial Intelligence; A Systems Approach" , 2008.
[9] D. Li and Y. Du "Artificial Intelligence With Uncertainty" , 2008.

Description:

There are many cognitive tasks that people can do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course will cover problem solving, automated reasoning, planning, and knowledge representation.

Course Topics:

• Introduction
• Agents and Intelligent Agents
• Search Methods(Uninformed and Informed)
• Constraint Satisfaction Problems(CSP)
• Games Playing(Adversarial Search)
• Knowledge Representation Methods
• Logic and Inference with Logic
• Reasoning with Uncertainty
• Soft Computing(FL, NC, GA,...)
• Planning Methods
• Some Applications of AI

Grade Distribution:

Your performance will be assessed by assignments, quizzes, class participation and final exam. Their weights are:

• Final Exam: 90%
• Mid-Term(take-home): 5%
• Quizzes and Class Participation: 5%

Assignments have to be typed, if you want them corrected. All of students must work on assignments individually. The assignments play a crucial part in understanding the course material and must be turned in by class time on the due date for full credit.
Participation means coming to class, asking questions, taking part in discussions and so on.

Useful Links:

Dictionaries and FAQs:

• AI FAQ
• AI Dictionary
• Glossary of AI terms
• The Prolog Dictionary
• Glossary of First-Order Logic
• The Machine Learning Dictionary
• Web Dictionary of Cybernetics and Systems
• Statistical Research and Training Center(SRTC)

Turing Test:

• Turing Test
• The Loebner Prize
• Definition of Turing Test
• The Turing Test (Stanford Encyclopedia of Philosophy)
• Machine Intelligence, Part I:The Turing Test and Loebner Prize

Artificial Intelligence:

• AI Overview
• AI on The WEB
• AI on The WEB
• AAAI Home Page
• Journal of AI Research
• Russel Book Web Page
• Comparison Text Books

Courses and Lecture Notes:

• AI Coursenotes
• Advanced Artificial Intelligence
• COMP9414: Artificial Intelligence
• CS4550/5550 ARTIFICIAL INTELLIGENCE (AI)

eBooks:

• Artificial Intelligence Programming With Ruby

Agents:

• Agents
• Microsoft Agent
• The Agent LaunchPad
• AI AGENT Download Page
• Intelligent Agents Group (IAG)
• Software Agents: An Overview
• Autonomous Agents as Embodied AI
• Where Do Intelligent Agents Come From?
• VisualBots: Visual Programming for Agent Based Simulation
• Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents

Searchs:

• Search Methods
• Tabu Search Web Resources
• An Introduction to Tabu Search By Michel Gendreau
• Search (A Subtopic of Reasoning)
• Genetic Algorithms (by John H. Holland)
• In A*, why does the heuristic have to always underestimate?

Games and Puzzles:

• Games & Puzzles
• IBM's Chess Playing Computer versus Kasparov
• A discussion about Deep Blue and Men vs Machines

Robotics :

• Robotics Links
• Boston Dynamics: The Leader in Lifelike Human Simulation

Machine Learning:

• Machine Learning
• Tools for learning Computational Intelligence

Soft Computing:

• Fuzzy Logic (by Philip Yam and Soumik Pal)
• Fuzzy Logic (by Alli Kator's, Pawel Przytycki's, and Henrey Scher's )
• Fuzzy Logic in Decision Making and Signal Processing
• Computational Intelligence in Control Engineering (by Robert E. King)
• FIP = Fuzzy Image Processing
• Free Artificial Neural Network Software - Dawson, University of Alberta
• Simulated Annealing Information
• Web Resources; Simulated Annealing
• Genetic Algorithms (by John H. Holland)
• Genetic Algorithms; Web Resources
• Memetic Algorithms' Home Page
• Ant Algorithms
• Ant Colony Optimization
• Probability Theory (by Peter Webb)
• Reinforcement Learning: An Introduction (by Richard S. Sutton and Andrew G. Barto )
• A Brief Introduction to Graphical Models and Bayesian Networks (By Kevin Murphy, 1998 )

Peoples:

• Kevin Night
• Peter Norvig
• Stuart J. Russel
• Lotfi A. Zadeh
• Mehdi Dastani
• Marvin L. Minsky

If you have any comments and suggestions about the class and my teaching course, you can send me a mail.