Midterm Review

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

5. Uninformed Search Strategies • Uninformed search strategies use only the information available in the problem definition • Breadth-first search Uniform-cost search Depth-first search • Depth-limited 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 truecost 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. 1 2 3 4 5 6 7 8 5 8 4 2 1 7 3 6 Heuristic Function • Function h(N) that estimate the cost of the cheapest path from node N to goal node. • Example: 8-puzzle h2(N) = sum of the distances of every tile to its goal position = 2 + 3 + 0 + 1 + 3 + 0 + 3 + 1 = 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