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Touring problems

Touring problems . Start from Arad, visit each city at least once. What is the state-space formulation? Start from Arad, visit each city exactly once. What is the state-space formulation? Two techniques for modeling constraints Invalidate states Precondition actions.

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Touring problems

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  1. Touring problems • Start from Arad, visit each city at least once. • What is the state-space formulation? • Start from Arad, visit each city exactly once. • What is the state-space formulation? • Two techniques for modeling constraints • Invalidate states • Precondition actions

  2. Uninformed search strategies • Uninformed search strategies use only the information available in the problem definition

  3. Tree search algorithms • Basic idea: • offline, simulated exploration of state space by generating successors of already-explored states (a.k.a.~expanding states)

  4. Tree search example

  5. Tree search example

  6. Tree search example

  7. Implementation: states vs. nodes • A state is a (representation of) a physical configuration • A node is a data structure constituting part of a search tree includes state, parent node, action, path costg(x), depth • The Expand function creates new nodes, filling in the various fields and using the SuccessorFn of the problem to create the corresponding states. Two different nodes may refer to the same state (but on different paths)!

  8. Implementation: general tree search

  9. Breadth-first search • Expand shallowest unexpanded node • Implementation: • fringe is a FIFO queue, i.e., new successors go at end

  10. Breadth-first search • Expand shallowest unexpanded node • Implementation: • fringe is a FIFO queue, i.e., new successors go at end

  11. Breadth-first search • Expand shallowest unexpanded node • Implementation: • fringe is a FIFO queue, i.e., new successors go at end

  12. Breadth-first search • Expand shallowest unexpanded node • Implementation: • fringe is a FIFO queue, i.e., new successors go at end

  13. Repeated states • Failure to detect repeated states can turn a linear problem into an exponential one!

  14. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  15. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front CS 3243 - Blind Search

  16. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front CS 3243 - Blind Search

  17. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front CS 3243 - Blind Search

  18. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  19. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  20. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  21. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  22. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  23. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  24. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  25. Depth-first search • Expand deepest unexpanded node • Implementation: • fringe = LIFO queue, i.e., put successors at front

  26. Which one is better? • BFS or DFS? CS 3243 - Blind Search

  27. Search strategies • A search strategy is defined by picking the order of node expansion • Strategies are evaluated along the following dimensions: • completeness: does it always find a solution if one exists? • time complexity: number of nodes generated • space complexity: maximum number of nodes in memory • optimality: does it always find a least-cost solution? • Time and space complexity are measured in terms of • b: maximum branching factor of the search tree • d: depth of the least-cost solution (assumed to be finite) • m: maximum depth of the state space (may be ∞) • Typical scenario: d is finite, but m is not

  28. Properties of breadth-first search • Complete?Yes (if b is finite) • Time?1+b+b2+b3+… +bd + b(bd-1) = O(bd+1) • Space?O(bd+1) (keeps every node in memory) • Optimal? Yes (if cost = 1 per step) • Space is the bigger problem (more than time)

  29. Properties of depth-first search • Complete? No: fails in infinite-depth spaces, spaces with loops • Modify to avoid repeated states along path  complete in finite spaces • Time?O(bm): terrible if m is much larger than d • but if solutions are dense, may be much faster than breadth-first • Space?O(bm), i.e., linear space! • Optimal? No

  30. Uniform-cost search • Expand least-cost unexpanded node • Implementation: • fringe = queue ordered by path cost • Equivalent to breadth-first if step costs all equal • Complete? Yes, if step cost ≥ ε • Time? # of nodes with g ≤ cost of optimal solution, O(bceiling(C*/ ε)) where C* is the cost of the optimal solution • Space? # of nodes with g≤ cost of optimal solution, O(bceiling(C*/ ε)) • Optimal? Yes – nodes expanded in increasing order of g(n)

  31. To summarize … • BFS: memory very high, complexity lower, optimal • DFS: memory very low, complexity higher (maybe lower if solutions are dense), not optimal • Is there an uninformed search that combines the advantages of these methods? CS 3243 - Blind Search

  32. Depth-limited search = depth-first search with depth limit L, i.e., nodes at depth L have no successors • How to determine a good depth? • Recursive implementation:

  33. Iterative deepening search

  34. Iterative deepening search l =0

  35. Iterative deepening search l =1

  36. Iterative deepening search l =2

  37. Iterative deepening search l =3

  38. Iterative deepening search • Number of nodes generated in a depth-limited search to depth d with branching factor b: NDLS = b0 + b1 + b2 + … + bd-2 + bd-1 + bd • Number of nodes generated in an iterative deepening search to depth d with branching factor b: NIDS = (d+1)b0 + d b^1 + (d-1)b^2 + … + 3bd-2 +2bd-1 + 1bd • For b = 10, d = 5, • NDLS = 1 + 10 + 100 + 1,000 + 10,000 + 100,000 = 111,111 • NIDS = 6 + 50 + 400 + 3,000 + 20,000 + 100,000 = 123,456 • Overhead = (123,456 - 111,111)/111,111 = 11%

  39. Properties of iterative deepening search • Complete? Yes • Time?(d+1)b0 + d b1 + (d-1)b2 + … + bd = O(bd) • Space?O(bd) • Optimal? Yes, if step cost = 1

  40. Summary of algorithms Demo of search algorithms http://www.cs.rochester.edu/~kautz/Mazes/search_algorithm_demo.htm

  41. Bi-directional search

  42. Bi-directional search • Pros and cons • Pros • O(bd) to O(bd/2) in terms of time • b = 10, d= 6, reduce from 11,111,100 to 22,200 • Cons • memory requirement, O(bd/2) • difficult to come up with predecessor function • implicit goal test

  43. Graph search CS 3243 - Blind Search

  44. Graph search • Will a node be generated twice? • Yes • Will a node be expanded twice? • No CS 3243 - Blind Search

  45. Graph search CS 3243 - Blind Search

  46. Optimality of graph search • Uniform search • BFS • DFS CS 3243 - Blind Search

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