CS.462 Artificial Intelligence

1. CS.462Artificial Intelligence SOMCHAI THANGSATHITYANGKUL Lecture 03 : Search (2)

2. Uniform Cost Search (UCS) • Uniform Cost Search: • Nodes in agenda keep track of total path length from start to that node • Agenda kept in priority queue ordered by path length • Get shortest path in queue

3. Uniform Cost Search (UCS) Graph: 2

4. UCS Agenda:

5. O 151 F 71 S 99 Z 75 211 90 A 140 R B P 97 120 101 118 146 138 D 75 M T 111 70 L C Try this Find a path from node A to the goal node B. Use UCS method.

6. O 151 F 71 S 99 Z 75 211 90 A 140 R B P 97 120 101 118 146 138 D 75 M T 111 70 L C UCS A Z75 T118 S140 T118 S140 O146 S140 O146L229

7. Informed Search • If we use uniform cost search on this graph What’s wrong ?

8. Informed Search • Informed Search or Heuristic Search methods try to estimate the “distance” to a goal state. A heuristic function h(s) is the estimated distance to the goal state. • Best-first greedy search - use h(s) instead of g(s) - Always expand the node with the least h(s) - Use a priority queue: Push in states with their second-half-cost h(s) Pop out the state with the least h(s) first.

9. Best-first greedy search Graph:

10. Best-first greedy search Agenda:

11. Try this Use Best first greedy search path from Arad to Buchraest

12. A* Search

13. A* Search • Evaluation function f(n)= g(n)+ h(n) • g(n) the cost (from the start) to reach the node n. • h(n) estimated cost to get from the node n to the goal. • f(n) estimated total cost of path through n to goal.

14. A* Search Graph:

15. A* Search Agenda:

16. Try this Use A* search path from Arad to Buchraest