Review: Search problem formulation
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Transcript of Review: Search problem formulation
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Review: Search problem formulation
• Initial state• Actions• Transition model• Goal state• Path cost
• What is the optimal solution?• What is the state space?
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Review: Tree search• Initialize the fringe using the starting state• While the fringe is not empty– Choose a fringe node to expand according to search strategy– If the node contains the goal state, return solution– Else expand the node and add its children to the fringe
• To handle repeated states:– Keep an explored set; add each node to the explored set every
time you expand it– Every time you add a node to the fringe, check whether it
already exists in the fringe with a higher path cost, and if yes, replace that node with the new one
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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?– Optimality: does it always find a least-cost solution?– Time complexity: number of nodes generated– Space complexity: maximum number of nodes in memory
• Time and space complexity are measured in terms of – b: maximum branching factor of the search tree– d: depth of the optimal solution– m: maximum length of any path in the state space (may be infinite)
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Uninformed search strategies
• Uninformed search strategies use only the information available in the problem definition
• Breadth-first search• Uniform-cost search• Depth-first search• Iterative deepening search
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Breadth-first search
• Expand shallowest unexpanded node• Implementation:– fringe is a FIFO queue, i.e., new successors go at end
A
D F
B C
E G
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Breadth-first search
• Expand shallowest unexpanded node• Implementation:– fringe is a FIFO queue, i.e., new successors go at end
A
D F
B C
E G
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Breadth-first search
• Expand shallowest unexpanded node• Implementation:– fringe is a FIFO queue, i.e., new successors go at end
A
D F
B C
E G
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Breadth-first search
• Expand shallowest unexpanded node• Implementation:– fringe is a FIFO queue, i.e., new successors go at end
A
D F
B C
E G
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Breadth-first search
• Expand shallowest unexpanded node• Implementation:– fringe is a FIFO queue, i.e., new successors go at end
A
D F
B C
E G
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Properties of breadth-first search
• Complete? Yes (if branching factor b is finite)
• Optimal? Yes – if cost = 1 per step
• Time? Number of nodes in a b-ary tree of depth d: O(bd)(d is the depth of the optimal solution)
• Space? O(bd)
• Space is the bigger problem (more than time)
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Uniform-cost search• Expand least-cost unexpanded node• Implementation: fringe is a queue ordered by path cost (priority
queue)• Equivalent to breadth-first if step costs all equal
• Complete? Yes, if step cost is greater than some positive constant ε (we don’t want infinite
sequences of steps that have a finite total cost)• Optimal?
Yes – nodes expanded in increasing order of path cost• Time?
Number of nodes with path cost ≤ cost of optimal solution (C*), O(bC*/ ε)This can be greater than O(bd): the search can explore long paths consisting of
small steps before exploring shorter paths consisting of larger steps • Space?
O(bC*/ ε)
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Depth-first search
• Expand deepest unexpanded node• Implementation:– fringe = LIFO queue, i.e., put successors at front
A
D F
B C
E G
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Properties of depth-first search
• Complete?Fails in infinite-depth spaces, spaces with loopsModify to avoid repeated states along path
complete in finite spaces
• Optimal?No – returns the first solution it finds
• Time? Could be the time to reach a solution at maximum depth m: O(bm)Terrible if m is much larger than dBut if there are lots of solutions, may be much faster than BFS
• Space? O(bm), i.e., linear space!
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Iterative deepening search
• Use DFS as a subroutine1. Check the root2. Do a DFS searching for a path of length 13. If there is no path of length 1, do a DFS searching
for a path of length 24. If there is no path of length 2, do a DFS searching
for a path of length 3…
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Iterative deepening search
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Iterative deepening search
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Iterative deepening search
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Iterative deepening search
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Properties of iterative deepening search
• Complete?Yes
• Optimal?Yes, if step cost = 1
• Time?(d+1)b0 + d b1 + (d-1)b2 + … + bd = O(bd)
• Space?O(bd)
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Informed search
• Idea: give the algorithm “hints” about the desirability of different states – Use an evaluation function to rank nodes and
select the most promising one for expansion
• Greedy best-first search• A* search
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Heuristic function• Heuristic function h(n) estimates the cost of
reaching goal from node n• Example:
Start state
Goal state
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Heuristic for the Romania problem
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Greedy best-first search
• Expand the node that has the lowest value of the heuristic function h(n)
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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Properties of greedy best-first search
• Complete?No – can get stuck in loops
startgoal
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Properties of greedy best-first search
• Complete?No – can get stuck in loops
• Optimal? No
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Properties of greedy best-first search
• Complete?No – can get stuck in loops
• Optimal? No
• Time? Worst case: O(bm)Best case: O(bd) – If h(n) is 100% accurate
• Space?Worst case: O(bm)
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How can we fix the greedy problem?
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A* search
• Idea: avoid expanding paths that are already expensive• The evaluation function f(n) is the estimated total cost
of the path through node n to the goal:
f(n) = g(n) + h(n)
g(n): cost so far to reach n (path cost)h(n): estimated cost from n to goal (heuristic)
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A* search example
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A* search example
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A* search example
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A* search example
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A* search example
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A* search example
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Admissible heuristics
• A heuristic h(n) is admissible if for every node n, h(n) ≤ h*(n), where h*(n) is the true cost to reach the goal state from n
• An admissible heuristic never overestimates the cost to reach the goal, i.e., it is optimistic
• Example: straight line distance never overestimates the actual road distance
• Theorem: If h(n) is admissible, A* is optimal
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Optimality of A*
• Suppose A* terminates its search at n* • It has found a path whose actual cost f(n*) = g(n*) is
lower than the estimated cost f(n) of any path going through any fringe node
• Since f(n) is an optimistic estimate, there is no way n can have a successor goal state n’ with g(n’) < C*
n
n*f(n*) = C*
f(n) > C*
n' g(n') f(n) > C*
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Optimality of A*
• A* is optimally efficient – no other tree-based algorithm that uses the same heuristic can expand fewer nodes and still be guaranteed to find the optimal solution– Any algorithm that does not expand all nodes with
f(n) < C* risks missing the optimal solution
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Properties of A*• Complete?
Yes – unless there are infinitely many nodes with f(n) ≤ C*• Optimal?
Yes• Time?
Number of nodes for which f(n) ≤ C* (exponential)• Space?
Exponential
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Designing heuristic functions• Heuristics for the 8-puzzle
h1(n) = number of misplaced tilesh2(n) = total Manhattan distance (number of squares from
desired location of each tile)
h1(start) = 8h2(start) = 3+1+2+2+2+3+3+2 = 18
• Are h1 and h2 admissible?
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Heuristics from relaxed problems• A problem with fewer restrictions on the actions is
called a relaxed problem• The cost of an optimal solution to a relaxed problem is
an admissible heuristic for the original problem• If the rules of the 8-puzzle are relaxed so that a tile
can move anywhere, then h1(n) gives the shortest solution
• If the rules are relaxed so that a tile can move to any adjacent square, then h2(n) gives the shortest solution
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Heuristics from subproblems
• Let h3(n) be the cost of getting a subset of tiles (say, 1,2,3,4) into their correct positions
• Can precompute and save the exact solution cost for every possible subproblem instance – pattern database
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Dominance
• If h1 and h2 are both admissible heuristics and
h2(n) ≥ h1(n) for all n, (both admissible) then h2 dominates h1
• Which one is better for search?– A* search expands every node with f(n) < C* or
h(n) < C* – g(n)– Therefore, A* search with h1 will expand more nodes
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Dominance• Typical search costs for the 8-puzzle (average number of
nodes expanded for different solution depths):
• d=12 IDS = 3,644,035 nodesA*(h1) = 227 nodes A*(h2) = 73 nodes
• d=24 IDS ≈ 54,000,000,000 nodes A*(h1) = 39,135 nodes A*(h2) = 1,641 nodes
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Combining heuristics
• Suppose we have a collection of admissible heuristics h1(n), h2(n), …, hm(n), but none of them dominates the others
• How can we combine them?
h(n) = max{h1(n), h2(n), …, hm(n)}
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Weighted A* search
• Idea: speed up search at the expense of optimality
• Take an admissible heuristic, “inflate” it by a multiple α > 1, and then perform A* search as usual
• Fewer nodes tend to get expanded, but the resulting solution may be suboptimal (its cost will be at most α times the cost of the optimal solution)
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Example of weighted A* search
Heuristic: 5 * Euclidean distance from goalSource: Wikipedia
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Example of weighted A* search
Heuristic: 5 * Euclidean distance from goal
Source: Wikipedia
Compare: Exact A*
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Memory-bounded search
• The memory usage of A* can still be exorbitant• How to make A* more memory-efficient while
maintaining completeness and optimality?
• Iterative deepening A* search• Recursive best-first search, SMA*– Forget some subtrees but remember the best f-value in these
subtrees and regenerate them later if necessary
• Problems: memory-bounded strategies can be complicated to implement, suffer from “thrashing”
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Uninformed search strategiesAlgorithm Complete? Optimal? Time
complexitySpace
complexity
BFS
UCS
DFS
IDS
b: maximum branching factor of the search treed: depth of the optimal solutionm: maximum length of any path in the state spaceC*: cost of optimal solution
Yes
Yes
No
Yes
If all step costs are equal
If all step costs are equal
Yes
No
O(bd)
O(bm)
O(bd)
O(bd)
O(bm)
O(bd)
Number of nodes with g(n) ≤ C*
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All search strategiesAlgorithm Complete? Optimal? Time
complexitySpace
complexity
BFS
UCS
DFS
IDS
Greedy
A*
Yes
Yes
No
Yes
If all step costs are equal
If all step costs are equal
Yes
No
O(bd)
Number of nodes with g(n) ≤ C*
O(bm)
O(bd)
O(bd)
O(bm)
O(bd)
No NoWorst case: O(bm)
Yes Yes
Best case: O(bd)
Number of nodes with g(n)+h(n) ≤ C*