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## Solving Problem by Searching

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### Solving Problem by Searching Chapter 3 - continued Outline Best-first search Greedy best-first search A* search and its optimality Admissible and consistent ... – PowerPoint PPT presentation

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Title: Solving Problem by Searching

1
Solving Problem by Searching
• Chapter 3 - continued

2
Outline
• Best-first search
• Greedy best-first search
• A search and its optimality
• Heuristic functions

3
Review Tree and graph search
• Tree search can expand the same node more than
once loopy path Graph search avoid duplicate no
de expansion by storing all nodes explored
• Blind search (BFS, DFS, uniform cost search)
• A search strategy is defined by picking the order
of node expansion
Evaluate search algorithm by
• Completeness
• Optimality
• Time and space complexity

4
Best-first search
• Idea use an evaluation function f(n) for each
node
• estimate of "desirability"
• Expand most desirable unexpanded node
• Implementation
• Order the nodes in fringe in decreasing order of
desirability
• Special cases
• greedy best-first search
• A search

5
Romania with step costs in km
6
Greedy best-first search
• Evaluation function f(n) h(n) (heuristic)
• estimate of cost from n to goal
• e.g., hSLD(n) straight-line distance from n to
Bucharest
• Greedy best-first search expands the node that
appears to be closest to goal

7
Greedy best-first search example
8
Greedy best-first search example
9
Greedy best-first search example
10
Greedy best-first search example
11
Properties of greedy best-first search
• Complete? No can get stuck in loops (for tree
search), e.g., Iasi ? Neamt ? Iasi ? Neamt ?
• For graph search, if the state space is finite,
greedy best-first search is complete
• Time? O(bm), but a good heuristic can give
dramatic improvement
• Space? O(bm) -- keeps all nodes in memory
• Optimal? No the example showed a non-optimal
path

12
A search
• Idea avoid expanding paths that are already
expensive Algorithm is the same as the uniform cos
t search except the evaluation function is
different
• Evaluation function f(n) g(n) h(n)
• g(n) actual cost so far to reach n
• h(n) estimated cost from n to goal
• f(n) estimated total cost of path through n to
goal

13
A search example
14
A search example
15
A search example
16
A search example
17
A search example
18
A search example
19
• 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 hSLD(n) (never overestimates the actual
• Theorem If h(n) is admissible, A using
TREE-SEARCH is optimal

20
Optimality of A (proof)
• Suppose some suboptimal goal G2 has been
generated and is in the fringe. Let n be an
unexpanded node in the fringe such that n is on a
shortest path to an optimal goal G.
• f(G2) g(G2) since h(G2) 0
• g(G2) gt g(G) since G2 is suboptimal
• f(G) g(G) since h(G) 0
• f(G2) gt f(G) from above

21
Optimality of A (proof)
• Suppose some suboptimal goal G2 has been
generated and is in the fringe. Let n be an
unexpanded node in the fringe such that n is on a
shortest path to an optimal goal G.
• f(G2) gt f(G) from above
• h(n) h(n) since h is admissible
• g(n) h(n) g(n) h(n) g(G) f(G)
• f(n) f(G)
• Hence f(G2) gt f(n), and A will never select G2
for expansion

22
Consistent heuristics
• A heuristic is consistent if for every node n,
every successor n' of n generated by any action
a,
• h(n) c(n,a,n') h(n')
• If h is consistent, we have
• f(n') g(n') h(n')
• g(n) c(n,a,n') h(n')
• g(n) h(n) f(n)
• i.e., f(n) is non-decreasing along any path.
• Theorem If h(n) is consistent, A using
GRAPH-SEARCH is optimal

23
Optimality of A
• A expands nodes in order of increasing f value
• Contour i has all nodes with ffi, where fi lt
fi1

24
Properties of A
• Complete? Yes (unless there are infinitely many
nodes with f f(G) )
• Time? Exponential
• Space? Keeps all nodes in memory
• Optimal? Yes

25
• E.g., for the 8-puzzle
• h1(n) number of misplaced tiles
• h2(n) total Manhattan distance
• (i.e., no. of squares from desired location of
each tile)
• h1(S) ?
• h2(S) ?

26
• E.g., for the 8-puzzle
• h1(n) number of misplaced tiles
• h2(n) total Manhattan distance
• (i.e., no. of squares from desired location of
each tile)
• h1(S) ? 8
• h2(S) ? 31222332 18

27
Dominance
• If h2(n) h1(n) for all n (both admissible)
• then h2 dominates h1
• h2 is better for search
• Typical search costs (average number of nodes
expanded)
• d12 IDS 3,644,035 nodes A(h1) 227 nodes
A(h2) 73 nodes
• d24 IDS too many nodes A(h1) 39,135 nodes
A(h2) 1,641 nodes

28
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

29
Use multiple heuristic functions
• We have several heuristic functions h1(n), h2(n),
, hk(n).
• One could define a heuristic function h(n) by
• h(n) max h1(n), h2(n), , hk(n)

30
Summary
• Best-first search uses an evaluation function
f(n) to select a node for expansion
• Greedy best-first search uses f(n) h(n). It is
not optimal but efficient
• A search uses f(n) g(n) h(n)
• A is complete and optimal if h(n) is admissible
(consistent) for tree (graph) search
• Obtaining good heuristic function h(n) is
important one can often get good heuristics by
relaxing the problem definition, using pattern
databases, and by learning