1. Search Part I Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

2. Introduction Let’s review a goal-based agent called a “problem-solving agent” They have two characteristics: 1.Find sequences of actions to reach a goal. 2.They are uninformed.

4. Preliminaries We need to define two things: Goal Formulation Define objectives Problem Formulation Define actions and states

5. Problem Formulation Four components: 1.The initial state 2.Actions (successor function) 3.Goal test 4.Path Cost

6. Problem Formulation Comments: a. Removing detail from a representation is called “abstraction”. b. Good abstractions are key to solving problems. c. A solution is a path from initial state to goal.

7. Example

8. Other Examples Toy Problems: Vacuum World 8-puzzle 8-queens problem

12. Other Examples Real Problems Route-Finding Problem Robot Navigation Automatic Assembly Sequencing Protein Design Internet Searching

13. Problem Solving By Searching Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

14. Solutions We search through a search tree We expand new nodes to grow the tree There are different search strategies Nodes contain the following: state parent node action path cost depth

15. Search Tree Initial state Expanded nodes

16. Performance Four elements of performance: Completeness (guaranteed to find solution) Optimality (optimal solution?) Time Complexity Space Complexity

17. Performance Complexity requires three elements: a.Branching factor b b.Depth of the shallowest goal node d c.Maximum length of any path m

18. Problem Solving By Searching Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

19. Breadth-First Search Root is expanded first Then all successors at level 2. Then all successors at level 3, etc. Properties:  Complete (if b and d are finite)  Optimal (if path cost increases with depth)  Complexity is O(b d+1 )

20. Search

21. Implemented as a FIFO Queue

22. Uniform-Cost Search Like breadth-first search Expand node with lowest path cost. Properties:  Complete (if b and d are finite)  Optimal (if path cost increases with depth)  Could be worse than breadth first search

23. Search

24. Depth-First Search Expand the deepest node at the bottom of the tree. Properties:  Incomplete  suboptimal  Time complexity is O(b m )  Space complexity is only O(bm)

25. Search Failure in infinite spaces…

26. Search

27. Depth-Limited Like depth-first search but with depth limit L. Properties:  Incomplete (if L < d)  Nonoptimal (if L > d)  Time complexity is O(b L )  Space complexity is O(bL)

28. Iterative Deepening A combination of depth and breadth-first search. Gradually increases the limit L Properties:  Complete (if b and d are finite)  Optimal if path cost increases with depth  Time complexity is O(b d )

29. Search

31. Bidirectional Search Run two searches – one from the initial state and one backward from the goal. We stop when the two searches meet. Motivation:  Time complexity: (b d/2 + b d/2 ) < b d Searching backwards not easy.

33. Problem Solving By Searching Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

34. Avoiding Repeated States In some problems repeated states are unavoidable (example 8-Puzzle) Solution: Store all nodes already generated on a hash table.

35. Avoiding Repeated States

36. Problem Solving By Searching Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

37. Partial Information Knowledge of states or actions is incomplete. a.Sensorless problems b.Contingency Problems c.Exploration Problems

38. Problem Solving By Searching Introduction Solutions and Performance Uninformed Search Strategies Avoiding Repeated States Partial Information Summary

39. To search we need goal and problem formulation. A problem has initial state, actions, goal test, and path function. Performance measures: completeness, optimality, time and space complexity.

40. Summary Uninformed search has no additional knowledge of states: breadth and depth-first search, depth-limited, iterative deepening, bidirectional search. It is important to eliminate repeated states. In partially observable environments, one can deal with belief states.