1. Review session covering text book pages 145-150 and 155-157
A*: f(n)=g(n)+h(n)—f*(n), g*(n), h*(n) are the true path cost, whereas f(n), g(n), h(n) are estimations of the true cost.
Heuristics that find the shorts path to a goal are called admissible. How do we choose and compare admissible heuristics?
Compare the “counting tiles out of place” vs. the “tile Manhattan distances to correct position” heuristic for the 8-puzzle. Which is better and why?
Monotonicity:=algorithm consistently finds shortest path for pairs of states it encounters during the search: If a heuristic has this property, if the same state is encountered a second time, the second path is never optimal; e.g. the algorithm can terminate when it encounters the goal state the first time; note that A* terminates (see page 134) when it tries to expand the goal state: A* expands states in non-decreasing f-value order and for each expanded node g(n)=g*(n).
Does admissibility imply monotonicity? Interesting theoretical question!

2. Review session covering text book pages 145-150 and 155-157
The set of states expanded is smaller for a more informed heuristic---see figure 4.18!
Games: Search deep with simple heuristics vs. Search not so deep with sophisticated heuristics.
Minimax requires a 2-path analysis of the search space—explain!
What state generation order is most beneficiary for alpha-beta?

3. Relevant Material Exam1 Tu., Feb. 26, 2008
Remark: Exam is open-notes/open-books
Relevant Textbook pages: 1-4, 20-31, , , 132(Section 4.2)-161, ,
Relevant Transparencies: Introduction to AI: Dr. Eick's Introduction to AI; Heuristic Search: Luger Chapter 3a, Luger Chapter 3b, Eick Search1, Eick Search1b(new), Eick Search2, Luger Chapter 4a, Luger Chapter 4b, Luger Chapter 4c, Eick Search3. Machine Learning: Tom Mitchell's Introduction to Machine Learning, EC(EvoNet EC Introduction, Using EC to Solve TSP, Brief Introduction to Evolutionary Machine Learning, Luger Chapter 12)
Other Material: knowledge Nilsson’s backtracking algorithm, Mitchell’s the discipline of machine learning.