1. Problem-Solving by Searching Uninformed (Blind) Search Algorithms

2. Project 1 is out, check class homepage Due in two weeks 9/27/2010 Monday before class Projects for students in different groups (480/580/796) could be different later on

3. Problem-solving steps Search/Planning Execute Problem Formulate

4. Example: Romania Find a route from one city (Arad) to the other (Bucharest)

5. Problem formulation Also called child-generator (Zerind, Sibiu, Timisoara)

6. Selecting a state space

7. Vacuum world state space graph

8. Example: The 8-puzzle

9. Tree search algorithms Breadth-first search Uniform-cost search Depth-first search A* search Breadth-first search Uniform-cost search Depth-first search A* search fail key Goal test expand

10. Implementation of search algorithms Search algorithms differ based on the specific queuing function they use All search algorithms must do goal-test only when the node is picked up for expansion FIFO LIFO Priority FIFO LIFO Priority

11. Flowchart of search algorithms Initialize queue with the initial state Is the queue empty? Is this node a goal? Remove the first node from the queue No Generate children and add them into the queue according to some strategy No Yes Return fail Yes Return node

12. Arad Sibiu Timisoara Zerind Arad Fagaras Oradea R.V. ??? AFORVTZ STZ A Is empty? Remove first Is goal? Expand & add Is empty? Remove first Is goal? Expand & add Initialize

13. Uninformed vs. informed search u No problem-specific knowledge about states u Can only distinguish a goal state from a non-goal state u Strategies that know whether one non-goal state is “more promising” than another are called informed (heuristic) search

14. Implementation: states vs. nodes

15. Evaluation

16. Uninformed search strategies u Also called blind search u Can only distinguish goal state and non-goal state u Do not know which state is more “promising” u Breadth-first search u Uniform-cost search u Depth-first search u Depth-limited search u Iterative deepening depth-first search

17. Breadth-first search Expand node with the smallest depth first

18. Initialize queue with the initial state Is the queue empty? Is this node a goal? Remove the first node from the queue No Generate children and add them into the queue according to some strategy No Yes Return fail Yes Return node Where should the new nodes be added in BFS?

19. Some strategy: A

20. BC

21. CDE

22. DEFG

28. Example of breadth-first search u Memory requirements are a bigger problem than is the time u Exponential-complexity search problems cannot be solved by uninformed methods for any but the smallest instances