1. Search Problems Russell and Norvig: Chapter 3, Sections 3.1 – 3.3 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm by Prof. Jean-Claude Latombe

2. Why Search? In many tasks, we know what a solution looks like, but do not have an algorithm that produces a solution. Goal + preferences Search is a general problem solving technique for this kind of situations. Search Problem: Input: Initial State + Goal (utility) + actions (state transitions) Output: A sequence of actions to reach the goal.

3. Example: Travel Task

4. Clean House Task

5. Vacuum Cleaner Space

6. Search Problem State space Initial state Successor function Goal test Path cost

7. Example: 8-queens Place 8 queens in a chessboard so that no two queens are in the same row, column, or diagonal. A solutionNot a solution

8. Example: 8-queens Formulation #1: States: any arrangement of 0 to 8 queens on the board Initial state: 0 queens on the board Successor function: add a queen in any square Goal test: 8 queens on the board, none attacked  64 8 states with 8 queens

9. Example: 8-queens Formulation #2: States: any arrangement of k = 0 to 8 queens in the k leftmost columns with none attacked Initial state: 0 queens on the board Successor function: add a queen to any square in the leftmost empty column such that it is not attacked by any other queen Goal test: 8 queens on the board  2,067 states

10. Example: Robot navigation What is the state space ?

11. Example: Robot navigation Cost of one horizontal/vertical step = 1 Cost of one diagonal step =  2

12. Example: Robot navigation

14. Cost of one step = ???

15. Example: Robot navigation

17. Cost of one step: length of segment

18. Example: Robot navigation

19. Example: Assembly Planning Initial state Goal state Successor function: Merge two subassemblies Complex function: it must find if a collision-free merging motion exists

20. Example: Assembly Planning

22. Assumptions in Basic Search The environment is static The environment is discretizable The environment is observable The actions are deterministic  open-loop solution

23. Search Problem Formulation Real-world environment  Abstraction Validity:  Can the solution be executed?

24. Search Problem Formulation Real-world environment  Abstraction Validity:  Can the solution be executed?  Does the state space contain the solution? Usefulness  Is the abstract problem easier than the real- world problem? Without abstraction an agent would be swamped by the real world

25. Search Problem Variants One or several initial states One or several goal states The solution is the path or a goal node In the 8-puzzle problem, it is the path to a goal node In the 8-queen problem, it is a goal node Any, or the best, or all solutions

26. Multi-state Search In vacuum problem, what if we don’t have sensors to tell us whether the floor is clean or not, and which room are we in?

28. Important Parameters Number of states in state space Size of memory needed to store a state Running time of the successor function

29. Applications Route finding: airline travel, telephone/computer networks Pipe routing, VLSI routing Pharmaceutical drug design Robot motion planning Video games

30. Summary Problem-solving agent State space, successor function, search Examples: Travel Task, house cleaning, 8-queens, robot navigation, assembly planning Assumptions of basic search Important parameters