CS B551: ELEMENTS OF
ARTIFICIAL INTELLIGENCE
1
Instructor: Kris Hauser
http://cs.indiana.edu/~hauserk
AGENDA
Searching, data structures, and algorithms
 Breadth-first search
 Depth-first search
 Uniform-cost search

2
DEFINING A SEARCH PROBLEM
S
 State space S
 Successor function:
x  S  SUCC(x)  2S
 Initial state s0
 Goal test:
xS  GOAL?(x) =T or F
 Arc cost
3
STATE GRAPH



Each state is
represented by a
distinct node
An arc (or edge)
connects a node s
to a node s’ if
s’  SUCC(s)
The state graph may
contain more than one
connected component
4
SOLUTION TO THE SEARCH PROBLEM




A solution is a path
connecting the initial
node to a goal node
(any one)
The cost of a path is
the sum of the arc
costs along this path
An optimal solution is
a solution path of
minimum cost
There might be
no solution !
G
I
5
SEARCH GRAPH != STATE GRAPH
8
2
3
4
7
5
1
6
8
2
7
3
4
5
1
If states are allowed to be revisited,
the search tree may be infinite even
when the state space is finite
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8
2
8
2
8
3
4
7
3
4
7
3
5
1
6
5
1
6
5
4
1
2
8
2
7
3
4
6
5
1
7
6
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SEARCH GRAPH NODE != STATE
8
2
3
4
7
5
1
6
STATE
PARENT-NODE
BOOKKEEPING
CHILDREN
...
Action
Right
Depth
5
Path-Cost 5
Expanded
yes
Depth of a node N
= length of path from root to N
(depth of the root = 0)
7
FRINGE OF SEARCH TREE

The fringe is the set of all search nodes that
haven’t been expanded yet
8 2
3 4 7
5 1 6
8 2 7
3 4
5 1 6
8
2
3 4 7
5 1 6
8 2
3 4 7
5 1 6
8 2 7
3
4
5 1 6
8 2
3 4 7
5 1 6
SEARCH STRATEGY
The fringe is the set of all search nodes that
haven’t been expanded yet
 The fringe is implemented as a priority queue
FRINGE

INSERT(node,FRINGE)
 REMOVE(FRINGE)


The ordering of the nodes in FRINGE defines the
search strategy
9
SEARCH ALGORITHM #1
SEARCH#1
1. If GOAL?(initial-state) then return initial-state
2. INSERT(initial-node,FRINGE)
3. Repeat:
4. If empty(FRINGE) then return failure
5. N  REMOVE(FRINGE)
Expansion of N
6. s  STATE(N)
7. For every state s’ in SUCCESSORS(s)
8.
Create a new node N’ as a child of N
9.
If GOAL?(s’) then return path or goal state
10.
INSERT(N’,FRINGE)
10
BLIND SEARCH STRATEGIES
11
BLIND STRATEGIES

Breadth-first


Bidirectional
Depth-first
Arc cost = 1
Depth-limited
 Iterative deepening


Uniform-Cost
(variant of breadth-first)
Arc cost
= c(action)    0
12
BREADTH-FIRST STRATEGY

New nodes are inserted at the end of FRINGE
1
2
4
FRINGE = (1)
3
5
6
7
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BREADTH-FIRST STRATEGY

New nodes are inserted at the end of FRINGE
1
2
4
FRINGE = (2, 3)
3
5
6
7
14
BREADTH-FIRST STRATEGY

New nodes are inserted at the end of FRINGE
1
2
4
FRINGE = (3, 4, 5)
3
5
6
7
15
BREADTH-FIRST STRATEGY

New nodes are inserted at the end of FRINGE
1
2
4
FRINGE = (4, 5, 6, 7)
3
5
6
7
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PERFORMANCE MEASURES
Completeness
A search algorithm is complete if it finds a
solution whenever one exists
[What about the case when no solution exists?]
 Optimality
A search algorithm is optimal if it returns a
minimum-cost path whenever a solution exists
 Complexity
It measures the time and amount of memory
required by the algorithm

17
IMPORTANT PARAMETERS

Maximum number of successors of any state
 branching factor b of the search tree

Minimal length (≠ cost) of a path between the
initial and a goal state
 depth d of the shallowest goal node in the
search tree
18
EVALUATION
b: branching factor
 d: depth of shallowest goal node
 Breadth-first search is:

Complete? Not complete?
 Optimal? Not optimal?

19
EVALUATION
b: branching factor
 d: depth of shallowest goal node
 Breadth-first search is:

Complete
 Optimal if step cost is 1


Number of nodes generated:
???
20
EVALUATION
b: branching factor
 d: depth of shallowest goal node
 Breadth-first search is:

Complete
 Optimal if step cost is 1


Number of nodes generated:
1 + b + b2 + … + bd = ???
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EVALUATION
b: branching factor
 d: depth of shallowest goal node
 Breadth-first search is:

Complete
 Optimal if step cost is 1

Number of nodes generated:
1 + b + b2 + … + bd = (bd+1-1)/(b-1) = O(bd)
  Time and space complexity is O(bd)

22
TIME AND MEMORY REQUIREMENTS
d
2
4
6
8
10
12
14
# Nodes
111
11,111
~106
~108
~1010
~1012
~1014
Time
.01 msec
1 msec
1 sec
100 sec
2.8 hours
11.6 days
3.2 years
Memory
11 Kbytes
1 Mbyte
100 Mb
10 Gbytes
1 Tbyte
100 Tbytes
10,000 Tbytes
Assumptions: b = 10; 1,000,000 nodes/sec; 100bytes/node
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TIME AND MEMORY REQUIREMENTS
d
2
4
6
8
10
12
14
# Nodes
111
11,111
~106
~108
~1010
~1012
~1014
Time
.01 msec
1 msec
1 sec
100 sec
2.8 hours
11.6 days
3.2 years
Memory
11 Kbytes
1 Mbyte
100 Mb
10 Gbytes
1 Tbyte
100 Tbytes
10,000 Tbytes
Assumptions: b = 10; 1,000,000 nodes/sec; 100bytes/node
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REMARK

If a problem has no solution, breadth-first may
run forever (if the state space is infinite or states
can be revisited arbitrary many times)
1
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3
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5
6
7
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9
10
11
12
13
14
15
?
1
2
3
4
5
6
7
8
9
10
11
12
13
15
14
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BIDIRECTIONAL STRATEGY
2 fringe queues: FRINGE1 and FRINGE2
s
Time and space complexity is O(bd/2)  O(bd)
if both trees have the same branching factor b
Question: What happens if the branching factor
is different in each direction?
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DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
FRINGE = (1)
3
5
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DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
FRINGE = (2, 3)
3
5
28
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
FRINGE = (4, 5, 3)
3
5
29
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
30
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
31
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
32
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
33
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
34
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
35
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
36
DEPTH-FIRST STRATEGY

New nodes are inserted at the front of FRINGE
1
2
4
3
5
37
EVALUATION
b: branching factor
 d: depth of shallowest goal node
 m: maximal depth of a leaf node
 Depth-first search is:



Complete?
Optimal?
38
EVALUATION
b: branching factor
 d: depth of shallowest goal node
 m: maximal depth of a leaf node
 Depth-first search is:



Complete only for finite search tree
Not optimal
Number of nodes generated (worst case):
1 + b + b2 + … + bm = O(bm)
 Time complexity is O(bm)
 Space complexity is O(bm) [or O(m)]
 [Reminder: Breadth-first requires O(bd) time and
space]

39
DEPTH-LIMITED SEARCH


Depth-first with depth cutoff k (depth at which
nodes are not expanded)
Three possible outcomes:
Solution
 Failure (no solution)
 Cutoff (no solution within cutoff)

40
ITERATIVE DEEPENING SEARCH


Provides the best of both breadth-first and
depth-first search
Main idea:
Totally horrifying !
IDS
For k = 0, 1, 2, … do:
Perform depth-first search with
depth cutoff k
(i.e., only generate nodes with depth  k)
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ITERATIVE DEEPENING
42
ITERATIVE DEEPENING
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ITERATIVE DEEPENING
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PERFORMANCE

Iterative deepening search is:
Complete
 Optimal if step cost =1

Time complexity is:
(d+1)(1) + db + (d-1)b2 + … + (1) bd = O(bd)
 Space complexity is: O(bd) or O(d)

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CALCULATION
db + (d-1)b2 + … + (1) bd
= bd + 2bd-1 + 3bd-2 +… + db
= (1 + 2b-1 + 3b-2 + … + db-d)bd
 (Si=1,…, ib(1-i))bd = bd (b/(b-1))2
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NUMBER OF GENERATED NODES (BREADTHFIRST & ITERATIVE DEEPENING)

d = 5 and b = 2
BF
1
2
4
8
16
32
63
ID
1x6=6
2 x 5 = 10
4 x 4 = 16
8 x 3 = 24
16 x 2 = 32
32 x 1 = 32
120
120/63 ~ 2
47
NUMBER OF GENERATED NODES (BREADTHFIRST & ITERATIVE DEEPENING)

d = 5 and b = 10
BF
1
10
100
1,000
10,000
100,000
111,111
ID
6
50
400
3,000
20,000
100,000
123,456
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123,456/111,111 ~ 1.111
RECAP

BFS: Complete, optimal


DFS: Not complete nor optimal


O(bd) time and space
O(bd) space, unbounded time
ID: Complete, optimal

O(bd) space, O(bd) time
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RELATED TOPICS
Non-unit costs
 Revisited states
 Heuristic search

50
NON-UNIT COSTS

Each arc has cost c   > 0
Why?
 The cost of the path to each node N is
g(N) = S costs of arcs
 Breadth-first is no longer optimal
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UNIFORM-COST SEARCH

Expand node in FRINGE with the cheapest path
A
S
1
S
10
5 B 5 G
15
C
A
5
Suboptimal path!
G
1
11
B
G
0
5
C
15
10
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SEARCH ALGORITHM #2
The goal test is applied
to a node when this node is
expanded, not when it is
generated.
SEARCH#2
1. If GOAL?(initial-state) then return initial-state
2. INSERT(initial-node,FRINGE)
3. Repeat:
4. If empty(FRINGE) then return failure
5. N  REMOVE(FRINGE)
6. s  STATE(N)
7. If GOAL?(s) then return path or goal state
8. For every state s’ in SUCCESSORS(s)
9.
Create a new node N’ as a child of N
10.
INSERT(N’,FRINGE)
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REVISITED STATES
No
Few
Many
1 2 3
search tree is finite search tree is infinite
4 5
7 8 6
8-queens
assembly
planning
8-puzzle and robot navigation
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AVOIDING REVISITED STATES


Requires comparing state descriptions
Breadth-first search:
Store all states associated with generated nodes in
VISITED
 If the state of a new node is in VISITED, then discard
the node

55
AVOIDING REVISITED STATES


Requires comparing state descriptions
Breadth-first search:
Store all states associated with generated nodes in
VISITED
 If the state of a new node is in VISITED, then discard
the node

Implemented as hash-table
or as explicit data structure with flags
56
EXPLICIT DATA STRUCTURES
57




Robot navigation
VISITED: array initialized to 0, matching grid
When grid position (x,y) is visited, mark
corresponding position in VISITED as 1
Size of the entire state space!
HASH TABLES
0
1
VISITED: Hash table of size N
2
N-1
Hash function: map
from S to {0,…,N-1}
8
2
3
4
5
1
1
2
7
4
5
6
7
8
3
6
If hash function is chosen carefully to minimize chance of collision,
then membership testing on M states averages O(M/N) time
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AVOIDING REVISITED STATES

Depth-first search:

Solution 1:
Store all states associated with nodes in current path in
VISITED
 If the state of a new node is in VISITED, then discard the
node


??
59
AVOIDING REVISITED STATES

Depth-first search:

Solution 1:
Store all states associated with nodes in current path in
VISITED
 If the state of a new node is in VISITED, then discard the
node



Only avoids loops
Solution 2:
Store all generated states in VISITED
 If the state of a new node is in VISITED, then discard the
node


Same space complexity as breadth-first !
60
AVOIDING REVISITED STATES IN
UNIFORM-COST SEARCH


For any state S, when the first node N such that
STATE(N) = S is expanded, the path to N is the
best path from the initial state to S
So:
When a node is expanded, store its state into
VISITED
 When a new node N is generated:

If STATE(N) is in VISITED, discard N
 If there exits a node N’ in the fringe such that STATE(N’) =
STATE(N), discard the node -- N or N’ -- with the highestcost path

61
SEARCH ALGORITHM #3
SEARCH#3
1. If GOAL?(initial-state) then return initial-state
2. INSERT(initial-node,FRINGE)
3. Repeat:
4. If empty(FRINGE) then return failure
5.
N  REMOVE(FRINGE)
6.
s  STATE(N)
7.
Add s to VISITED
7.
If GOAL?(s) then return path or goal state
8.
For every state s’ in SUCCESSORS(s)
9.
Create a new node N’ as a child of N
10.
If s’ is in VISITED then discard N’
11.
If there is N’’ in FRINGE with STATE(N’)=STATE(N’’)
12.
If g(N’’) is lower than g(N’) then discard N’
13.
Otherwise discard N’’
14.
INSERT(N’,FRINGE)
62
HOMEWORK

Readings: R&N Ch. 3.5
63