1. Midterm Review

2. Tuesday, March 26
Cheat sheet
Calculator

3. Types of Questions T or F Short answers
Homework questions (frontier, explored set, ..)

4. Environment Types Homework 2 Fully-Observable? Single-Agent?
Deterministic?
Episodic?
Static?
Discrete?

5. Uninformed Search Strategies
Uninformed search strategies use only the information available in the problem definition
Depth-first search
Iterative deepening search

6. Informed Search Strategies
uses problem-specific knowledge beyond the definition of the problem itself
Greedy best-first search
A* search

7. Complete
Time
Space
Optimal

8. Admissible heuristics
A heuristic h(n) is admissible if for every node n, h(n) ≤ h*(n), where h*(n) is the true cost to reach the goal state from n.
An admissible heuristic never overestimates the cost to reach the goal
Example: hSLD(n) (never overestimates the actual road distance)

9. Manhattan distances

10. Heuristic Function
Function h(N) that estimate the cost of the cheapest path from node N to goal node.
Example: 8-puzzle 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
h2(N) = sum of the distances of
every tile to its goal position =
= 13
goal N

11. Local search algorithms
In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution.
e.g., n-queens.
We can use local search algorithms:
keep a single "current" state, try to improve it
generally move to neighbors
The paths are not retained

12. Hill-climbing search
Problem: depending on initial state, can get stuck in local maxima.

13. Simulated annealing
Local beam search
Genetic algorithms