AI Fall 2008

1. Course webpage
2. Syllabus
3. Assignments for 7th period
4. Python.org, Python tutorial (Guido van Rossum), Beginner's Guide, more documentation from the Python site
5. Lisp programming links
6. Example demos of course topics
   • Blind search algorithms, no heuristics, breadth-first, depth-first, depth-limited, iterative deepening
   • AStar demonstration, heuristic search, f = h + g, where h is the estimated cost to reach the goal, g is the actual cost so far in the search, f is the sum of these two.
   • Minimax links
     • Slides from Russell/Norvig (see slide 6 for a picture)
     • Minimax tree, Game of Nim (McGill Univ)
     • Minimax algorithm
     • Minimax picture - tic tac toe
   • genetic algorithms also GA's explained
7. More AI topics from our text
   • (these topics are covered) Chap 3-6 Searching, exploration, constraint satisfaction, adversarial search, genetic algorithms in the 8-queen problem, minimax trees, alpha-beta pruning
   • Chap 7-10 Knowledge and knowledge representation
   • Chap 13,14 - Uncertain knowledge, probabilistic reasoning
   • Chap 18 - Learning, decision trees
   • Chap 20, 21 - Statiscal learning methods, reinforcement learning
   • Chap 24 - perception, machine vision