Class Notes for AI

• What is AI? Chapter 1

• Representations are key. Three problem solving methodologies: generate and test, means-ends analysis, problem reduction. Chapter 2 and Chapter 3

• Finding a path and Tree Search. Blind search methods: DFS, BFS, NDFS Chapter 4

• Optimal Search, Game Trees, Minimax search, Alpha-Beta pruning. Chapters 4, 5 and 6.

• Rule Based systems. Lisp. Deduction systems, forward chaining, backward chaining. Reactions systems, Conflict resolution Chapter 7
  • Deductions system. Zookeeper
  • Reaction system. Bagger

• Forward and Backward Chaining. Variable binding. Chapter 7.

• Planning, AND-OR Trees and Problem Reduction

• Frames and Inheritance

• Review for Test 1

• Logic and Resolution proofs Chapter 13

• Resolution proofs require axioms to be in clause form Chapter 13.

• An introduction to Genetic Algorithms. Chapter 25.

• The Schema Theorem for Genetic Algorithms

• An introduction to robotics from Dr. Monica Nicolescu.

• An introduction to Neural Networks. Chapter 22. Recognize this?. Here is another view .

• My ACDM talk and an overview of research at UNR on GAs.