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Quiz 1 : Queue based search

Quiz 1 : Queue based search. q1a1 : Any complete search algorithm must have exponential (or worse) time complexity. q1a2 : DFS requires more space than BFS. q1a3 : BFS always returns the optimal cost path. q1a4: BFS uses a FIFO queue as fringe. q1a5: UCS uses a LIFO queue as fringe.

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Quiz 1 : Queue based search

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  1. Quiz 1 : Queue based search • q1a1: Any complete search algorithm must have exponential (or worse) time complexity. • q1a2: DFS requires more space than BFS. • q1a3: BFS always returns the optimal cost path. • q1a4: BFS uses a FIFO queue as fringe. • q1a5: UCS uses a LIFO queue as fringe. • q1a6: BFS has the best time complexity out of UCS, BFS, DFS, ID. • Yes • No • No • Yes • No • No

  2. CSE511a: Artificial IntelligenceSpring 2013 Lecture 3: A* Search 1/24/2012 Robert Pless – Wash U. Multiple slides over the course adapted from Kilian Weinberger, originally by Dan Klein (or Stuart Russell or Andrew Moore)

  3. Announcements • Projects: • Project 0 due tomorrow night. • Project 1 (Search) is out soon, will be due Thursday 2/7. • Find project partner • Try pair programming, not divide-and-conquer • Exercise more and try to eat more fruit.

  4. Today • A* Search • Heuristic Design

  5. Recap: Search • Search problem: • States (configurations of the world) • Successor function: a function from states to lists of (action, state, cost) triples; drawn as a graph • Start state and goal test • Search tree: • Nodes: represent plans for reaching states • Plans have costs (sum of action costs) • Search Algorithm: • Systematically builds a search tree • Chooses an ordering of the fringe (unexplored nodes)

  6. Machinarium

  7. Machinarium: Search Problem Youtube

  8. A*-Search

  9. General Tree Search

  10. Uniform Cost Search • Strategy: expand lowest path cost • The good: UCS is complete and optimal! • The bad: • Explores options in every “direction” • No information about goal location c  1 … c  2 c  3 Start Goal [demo: countours UCS]

  11. Best First (Greedy) b • Strategy: expand a node that you think is closest to a goal state • Heuristic: estimate of distance to nearest goal for each state • A common case: • Best-first takes you straight to the (wrong) goal • Worst-case: like a badly-guided DFS … b … [demo: countours greedy]

  12. Example: Heuristic Function h(x)

  13. Combining UCS and Greedy • Uniform-costorders by path cost, or backward cost g(n) • Best-firstorders by goal proximity, or forward cost h(n) • A* Search orders by the sum: f(n) = g(n) + h(n) 5 e h=1 1 1 3 2 S a d G h=6 h=5 1 h=2 h=0 1 c b h=7 h=6 Example: Teg Grenager

  14. Three Questions • When should A* terminate? • Is A* optimal? • What heuristics are valid?

  15. When should A* terminate? • Should we stop when we enqueue a goal? • No: only stop when we dequeue a goal A 2 2 h = 2 G S h = 3 h = 0 B 3 2 h = 1

  16. Is A* Optimal? 1 • What went wrong? • Actual bad goal cost < estimated good goal cost • We need estimates to be less than actual costs! A 3 h = 6 h = 0 S G h = 7 5

  17. Admissible Heuristics • A heuristic h is admissible(optimistic) if: where is the true cost to a nearest goal • Example: • Coming up with admissible heuristics is most of what’s involved in using A* in practice. 15

  18. Example: Pancake Problem Cost: Number of pancakes flipped

  19. Pancake: State Space Object

  20. Example: Pancake Problem State space graph with costs as weights 4 2 3 2 3 4 3 4 2 3 2 2 4 3

  21. Example: Heuristic Function Heuristic: the largest pancake that is still out of place 3 h(x) 4 3 4 3 0 4 4 3 4 4 2 3

  22. Example: Pancake Problem

  23. Optimality of A*: Blocking Notation: • g(n) = cost to node n • h(n) = estimated cost from n to the nearest goal (heuristic) • f(n) = g(n) + h(n) =estimated total cost via n • G*: a lowest cost goal node • G: another goal node …

  24. Optimality of A*: Blocking Proof: • What could go wrong? • We’d have to have to pop a suboptimal goal G off the fringe before G* • This can’t happen: • Imagine a suboptimal goal G is on the queue • Some node n which is a subpath of G* must also be on the fringe (why?) • n will be popped before G …

  25. Properties of A* Uniform-Cost A* b b … …

  26. UCS vs A* Contours • Uniform-cost expanded in all directions • A* expands mainly toward the goal, but does hedge its bets to ensure optimality Start Goal Start Goal [demo: countours UCS / A*]

  27. Creating Admissible Heuristics • Most of the work in solving hard search problems optimally is in coming up with admissible heuristics • Often, admissible heuristics are solutions to relaxed problems, where new actions are available • Inadmissible heuristics are often useful too (why?) 15 4

  28. Example: 8 Puzzle • What are the states? • How many states? • What are the actions? • What states can I reach from the start state? • What should the costs be?

  29. Search State

  30. 8 Puzzle I • Heuristic: Number of tiles misplaced • Why is it admissible? • h(start) = • This is a relaxed-problem heuristic 8

  31. 8 Puzzle II • What if we had an easier 8-puzzle where any tile could slide any direction at any time, ignoring other tiles? • Total Manhattan distance • Why admissible? • h(start) = 3 + 1 + 2 + … = 18 [demo: eight-puzzle]

  32. 8 Puzzle III • How about using the actual cost as a heuristic? • Would it be admissible? • Would we save on nodes expanded? • What’s wrong with it? • With A*: a trade-off between quality of estimate and work per node!

  33. Trivial Heuristics, Dominance • Dominance: ha≥ hc if • Heuristics form a semi-lattice: • Max of admissible heuristics is admissible • Trivial heuristics • Bottom of lattice is the zero heuristic (what does this give us?) • Top of lattice is the exact heuristic

  34. Other A* Applications • Pathing / routing problems • Resource planning problems • Robot motion planning • Language analysis • Machine translation • Speech recognition • …

  35. Graph Search

  36. Tree Search: Extra Work! • Failure to detect repeated states can cause exponentially more work. Why?

  37. S e e p d q h h r r b c p p q q f f a a q q c c G G a a Graph Search • In BFS, for example, we shouldn’t bother expanding the circled nodes (why?)

  38. Graph Search • Very simple fix: never expand a state twice

  39. Graph Search • Idea: never expand a state twice • How to implement: • Tree search + list of expanded states (closed list) • Expand the search tree node-by-node, but… • Before expanding a node, check to make sure its state is new • Python trick: store the closed list as a set, not a list • Can graph search wreck completeness? Why/why not? • How about optimality?

  40. Consistency 1 • What went wrong? • Taking a step must not reduce f value! A 1 h = 4 h = 1 h = 6 S C B 3 2 1 h = 1 G h = 0

  41. Consistency • Stronger than admissability • Definition: • C(A→C)+h(C)≧h(A) • C(A→C)≧h(A)-h(C) • Consequence: • The f value on a path never decreases • A* search is optimal A h = 4 1 h = 0 C G 3 h = 1

  42. Optimality of A* Graph Search Proof: • New possible problem: nodes on path to G* that would have been in queue aren’t, because some worse n’ for the same state as some n was dequeued and expanded first (disaster!) • Take the highest such n in tree • Let p be the ancestor which was on the queue when n’ was expanded • Assume f(p) ≦ f(n) (consistency!) • f(n) < f(n’) because n’ is suboptimal • p would have been expanded before n’ • So n would have been expanded before n’, too • Contradiction!

  43. Optimality • Tree search: • A* optimal if heuristic is admissible (and non-negative) • UCS is a special case (h = 0) • Graph search: • A* optimal if heuristic is consistent • UCS optimal (h = 0 is consistent) • Consistency implies admissibility • In general, natural admissible heuristics tend to be consistent

  44. Summary: A* • A* uses both backward costs and (estimates of) forward costs • A* is optimal with admissible heuristics • Heuristic design is key: often use relaxed problems

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