This serves as a guide for students preparing for the Graduate Comprehensive Exam in Artificial Intelligence. The topics below cover the fundamental concepts of artificial intelligence. They include areas in search, representation, reasoning, planning, learning, and AI systems.

Students need to be aware that some materials might not be covered by a particular instructor in the corresponding course and they are expected, as graduate students, to be able to read and understand materials they might not have seen in class.

Books:

• Primary book for the covered materials:
  • Russell, S. & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Second Edition, Prentice Hall. [AIMA]
• Alternate books:
  • Ginsberg, M. (1993). Essentials of Artifical Intelligence. Morgan Kaufmann. [EAI]
  • Winston, P. (1992). Artificial Intelligence. Addison-Wesley.
  • Luger, G. & Stubblefield, W. (1993). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Benjamin/Cummings.

Topics:

• Problem Solving
  • Search: Breadth-First, Depth-First, Iterative Deepening Depth-first, Best-First, Hill climing, and A* [AIMA Ch3-4.3]
  • Constraint Satisfaction [AIMA Ch5.1-2]
  • Game Playing: Minimax Trees and Alpha-Beta Pruning [AIMA 6.1-3]
• Knowledge and Reasoning
  • Propositional logic [AIMA Ch 7.1-5]
  • Predicate (first-order) logic [AIMA Ch 8.1-3]
  • Inference [AMIA Ch 9.1 - page 300]
• Planning [AIMA Ch11.1-3]
• Uncertain Knowledge and Reasoning: Bayesian Networks [AIMA Ch14.1-2]
• Learning
  • inductive learning: decision trees [AIMA Ch18.1-3]