Informed and Uninformed search Strategies

Prof. Amey D.S.Kerkar
Computer Engineering Department,
Don Bosco College of Engineering
Fatorda-Goa.

Control strategies
 Helps us decide which rule to apply next.
 What to do when there are more than 1 matching
rules?
 Good control strategy should:
1. cause motion
2.Systematic

Control strategies are classified as:
1. Uninformed/blind search control strategy
Do not have additional info about states beyond problem def.
Total search space is looked for solution
No info is used to determine preference of one child over
other.
Example: 1. Breadth First Search(BFS), Depth First
Search(DFS), Depth Limited Search (DLS).

A
B
C
E
D HF
G
State Space without any extra information associated with each state

2. Informed/Directed Search Control Strategy
Some info about problem space(heuristic) is used to
compute preference among the children for exploration
and expansion.
Examples: 1. Best First Search, 2. Problem Decomposition,
A*, Mean end Analysis
Heuristic function:
It maps each state to a numerical value which depicts
goodness of a node.
H(n)=value
Where ,
H() is a heuristic function and ‘n’ is the current state.

Ex: in travelling salesperson problem heuristic value
associated with each node(city) might reflect
estimated distance of the current node from the goal
node.
The heuristic we use here is called HSLD Straight line
Distance heuristic.

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Example of the state space with heuristic values associated with each state

Breadth First Search (BFS)
 Algorithm:
 1. Create a variable NODE_LIST and set it to
initial state.
 2.Until a Goal State is found or NODE_LIST is
empty:
 A) Remove the first element from NODE_LIST
amd call it as ‘E’. If the node list was empty then
Quit.
 B) For each way that each rule can match the state
described in ‘E’ do:
 i) Apply the rule to generate the new state
 Ii) If the new state is a goal state, quit and return this
state.
 Iii) otherwise add the new state at the end of
NODE_LIST.

 Consider the following State Space to be searched:
A
B
C
E
D HF
GG
Let A be the start state and G be the final or goal state to be searched.
NODE_LIST={A} A is not goal node it is expanded .

A
B
C
E
D HF
GG
NODE_LIST={B,C}
A

A
B
C
E
D HF
GG
NODE_LIST={C,D,E}
A
B

A
B
C
E
D HF
GG
NODE_LIST={D,E,G}
A
B
A
B
C

A
B
C
E
D HF
GG
NODE_LIST={E,G,F}
A
B
A
B
C
A
B
C
D

A
B
C
E
D HF
GG
NODE_LIST={G,F}
A
B
A
B
C
A
B
C
D
E

A
B
C
E
D HF
GG
NODE_LIST={G,F}
A
B
A
B
C
A
B
C
D
E
GOAL NODE FOUND!!

A
B
C
E
D HF
GG
NODE_LIST={G,F}
A
B
A
B
C
A
B
C
D
E
TRAVERSAL ORDER: A-B-C-D-E-G

Depth First Search
Algorithm:
1) If initial state is a goal state, quit and return success.
2) Otherwise do the following until success or failure is
reported:
a. Generate successor ‘E’ of the initial state. If there are no
more successors signal failure.
b. Call Depth-First-Search with ‘E’ as he start state. If there
are no more successors then , signal failure.
c. If success is obtained, return success, otherwise continue
in this loop.

A
B
C
E
D HF
GG
Consider the following Search Space:
DFS(A)

A
B
C
E
D HF
GG
DFS(B)
A
B

A
B
C
E
D HF
GG
DFS(E)
A
B
E

A
B
C
E
D HF
GG
DFS(B)
A
B
E

A
B
C
E
D HF
GG
DFS(D)
A
B
E
B
E

A
B
C
E
D HF
GG
DFS(F)
A
B
E
B
E

A
B
C
E
D HF
GG
DFS(H)
A
B
E
B
E

A
B
C
E
D HF
GG
DFS(G)
A
B
E
B
E
GOAL NODE FOUND!!

Advantages of BFS:
1. BFS is a systematic search strategy- all nodes at level n are
considered before going to n+1 th level.
2. If any solution exists then BFS guarentees to find it.
3. If there are many solutions , BFS will always find the
shortest path solution.
4. Never gets trapped exploring a blind alley
Disadvantages of BFS:
1. All nodes are to be generated at any level. So even
unwanted nodes are to be remembered. Memory
wastage.
2. Time and space complexity is exponential type- Hurdle.

Advantages of DFS:
1. Memory requirements in DFS are less compared to BFS as
only nodes on the current path are stored.
2. DFS may find a solution without examining much of the
search space of all.
Disadvantages of BFS:
1. This search can go on deeper and deeper into the search
space and thus can get lost. This is referred to as blind
alley.

Hill Climbing Algorithm
 Local Search Algorithm
 State space landscape
Objectivefunction
State space
X
X’
Global Maximum
Local Maximum
Y’
Y
Shoulder
P1P2
P2’
P1’

 Maximization function is called Objective Function
 Minimization function is called Heuristic Cost function
 Heuristic cost= distance, time, money spent
 Objective function= profit, success
HEURISTICCOSTFUNCTION
State space
Global Minimum
Local Minimum

Algorithm for Hill Climbing
1. Evaluate the initial state. If it is the goal state then
return and quit. Otherwise continue with initial state as
current state.
2. Loop until a solution is found or until there are no new
operators left to be applied to the current state:
a. Select operator that has not been applied to the current
state and apply it to produce the new state.
b. Evaluate the new state
i. If it is the goal state, then return and quit
ii. If it is not a goal state but it is better than the current state then
make it the current state.
iii. If it is not better than the current state then continue in the
loop.

 Example block world problem:
INITIAL STATE (P) GOAL STATE (Z)
HEURISTIC
FUNCTION- add one
point for every block
which is resting on the
thing that it must be
resting on.
1
1
1
1
1
1
1
1
H(Z)=8
1
1
1
1
H(P)=4

 The tree generated till now is:
 From initial state most natural move is:
p
H(P)=4
STATE (Q)
H(Q)= 6
The tree Generated till Now is:
p
H(P)=4
Q
H(Q)=6

STATE (Q)
Now from Q there are three states possible which are as follows:
STATE (R) STATE (S) STATE (T)
H(Q)= 6 H(Q)= 6 H(Q)= 6

Thus we have the tree until now as:
p
H(P)=4
R
H(Q)=6
Q
H(R)=4
S
H(S)=4
T
H(T)=4
We see that from state Q
When nodes R,S and T are
Generated, their value of
Heuristic is less compared
To heuristic value of Q itself.
Hill Climbing Algorithm
Has reached local maxima
Whereas in reality the actual
Goal node has heuristic
value of 8.
THIS IS A DISADVANTAGE
OF LOCAL SEARCH ALGORITHM!!

Advantages of Hill Climbing
Disadvantages of Hill Climbing
It can be used in continuous as well as discrete domains.
1. Not efficient method –not suitable to problems where the value of
heuristic function drops off suddenly when solution may be in sight.
2.Local search method- gets caught up in local maxima/minima.
Solution to Local Maxima problem:
1. Backtracking to some earlier node and try different direction.
2. Simulated annealing

Global Search Techniques:
1. Best First Search(OR graph)
 Where not only the current branch of the search space but
all the so far explored nodes/states in the search space are
considered in determining the next best state/node.
2. A* Algorithm
 Which is improvised version of Best first Search.
3. Problem Reduction and And-Or Graphs.
 AO* Algorithm.
4. Constraint Satisfaction Problem (CSP)
 Graph Colouring Problem and Crypt Arithmetic Problems.
5. Mean End Analysis (MEA)

Best First Search
 Heuristic based search technique.
 Every node in the search space has an Evaluation
function (heuristic function)associated with it.
 Evaluation function==heuristic cost function (in case
of minimization problem) OR objective function(in
case of maximization).
 Decision of which node to be expanded depends on
value of evaluation function.
 Evaluation value= cost/distance of current node from
goal node.
 For goal node evaluation function value=0

 Algorithm:
 Uses 2 lists:
1. OPEN- all those nodes that have been generated & have
had heuristic function applied to them but have not yet
been examined.
2. CLOSED- contains all nodes that have already been
examined.

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OPEN={ S}
CLOSED={}

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OPEN={ A(2),C(5),B(6)}
CLOSED={S}

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OPEN={ C(5), B(6), E(8), D(10)}
CLOSED={S,A}

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OPEN={ B(6), H(7),E(8), D(10)}
CLOSED={S,A, C}

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OPEN={H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B}

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OPEN={I(5), J(6),H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B,H}

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OPEN={L(0),K(1),M(1),I(5), J(6),H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B,H,I}

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OPEN={K(1),M(1),I(5), J(6),H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B,H,I,L}

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OPEN={K(1),M(1),I(5), J(6),H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B,H,I,L} GOAL NODE FOUND!!

 In Best First Search we jump all around in the search
graph to identify the nodes with minimal evaluation
function value.
 Gives faster solutions but still no guarantee.

A* Algorithm
 A* algorithm is similar to Best first Search.
 Only difference: Best First Search takes h(n) as
evaluation function/heuristic value.
 In Best first search h(n)= estimated cost of current
node ’n’ from the goal node.
 A* takes the evaluation function as:
 F(n)=g(n)+h(n)
 where,
g(n)= cost(or distance) of the current node from start
state.
h(n)= estimated cost of current node from goal node.

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OPEN={S}
CLOSED={}
2

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OPEN={A(5+2=7}, B(6+2=8), C(5+5=10)}
CLOSED={S}
2

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OPEN={B(8), C(10),E(7+8=15), D(9+10=19)}
CLOSED={S,A}
2

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OPEN={C(10),E(15), F(5+13=18),G(4+14=18), D(19)}
CLOSED={S,A,B}
2

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OPEN={E(15), F(18),G(18), D(19),H(5+7+7=19)}
CLOSED={S,A,B,C}
2

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OPEN={K(5+2+2+1=10), F(18),G(18), D(19),H(19)}
CLOSED={S,A,B,C,E}
2

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OPEN={ F(18),G(18), D(19),H(19), I(21+5=26)}
CLOSED={S,A,B,C,E,K}
2

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OPEN={ I(2+3+8+5=18),(F(18),G(18), D(19),H(19), I(26)}
CLOSED={S,A,B,C,E,K,F}
2

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OPEN={ M(2+3+8+1+1=15),F(18),G(18), D(19),H(19),L(21+0=21) ,I(26)}
CLOSED={S,A,B,C,E,K,F,I}
2

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OPEN={ L(2+3+8+1+2+0=16), F(18),G(18), D(19),H(19),L(21) ,I(26)}
CLOSED={S,A,B,C,E,K,F,I,M}
2

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OPEN={F(18),G(18), D(19),H(19),L(21) ,I(26)}
CLOSED={S,A,B,C,E,K,F,I,M,L}
2
GOAL FOUND!!

Problem Reduction and
Decomposition (AND-OR Graphs)
Goal:
Acquire TV
Goal: Steal a
TV set
Goal: earn
some money
Goal: buy
TV set

Constraint Satisfaction Problem
 Each state contains:
Variables X1,X2,X3,…….Xn
Constraints C1,C2,…..Cn
Variables have to be assigned with values V1,V2,…..Vn
Such that none of the constraints are violated.
 Goal state- one in which all variables are assigned resp.
values and those values do not violate any constraint

Example graph colouring problem
1 2 3
4 5 6
7
Variables: X1,X2,…….X7
Constraints: {red, green, blue}
1 2 3
4 5 6 7
8
8

Crypt Arithmetic
Constraints:
1. Variables: can take values from 0-9
2. No two variables should take same value
3. The values should be selected such a way that it
should comply with arithmetic properties.
T W O
+ T W O
______________________________
F O U R

C3 C2 C1
T W O
+ T W O
______________________________
F O U R
STEP 1:
C3 =1 since 2 single digit numbers plus a carry cannot be more
than 19 thus,
C3=1 F=1
1 C2 C1
T W O
+ T W O
______________________________
1 O U R
Thus,

STEP 2: T+T+C2 > 9 because only then it can generate carry.
C2 can be 0 or 1 , depending on: if previous column is
generating carry or not.
C2=1
T=5
Thus,
1 C2 C1
T W O
+ T W O
______________________________
1 O U R
Assume:
Then, 2T+1>9 So, 2T>8 hence T>4
T can take value from 4,5,6,…9
Assume:

STEP 3:
T=6
GOBACK TO STEP 2 AND ASSUME DIFFERENT VALUE FOR T
1 1 C1
5 W O
+ 5 W O
______________________________
1 O U R
We know , T can take value from 5,6,…9
Assume:
BUT , if T=5 , T+T+C2=11 which means O=1 !!! CONSTRAINT
VIOLATED as F=1.

STEP 4: T+T+C2 > 13 so,
1 C2 C1
6 W O
+ 6 W O
______________________________
1 O U R
O=3 Accepted till now
1 C2 C1
6 W 3
+ 6 W 3
______________________________
1 3 U R
O+O =R so, Since O=3, R=6 !!! VIOLATION as T=6
Hence T=6 cant generate Solution.

STEP 5:
1 C2 C1
7 W O
+ 7 W O
______________________________
1 O U R
O+O =R so, Since O=5, R= 0 and C1= 1
T=7Assume:
T+T+C2= 7+7+1=15 Thus, O=5
1 C2 C1
7 W 5
+ 7 W 5
______________________________
1 5 U R
O=5 R=0 C1=1

Since W+W+C1=U if W=5 then,
5+5+1= 11 Thus U=1 !!! VIOLATION as F=1 thus
W Cannot be 5 Repeat step 7.
W=5
1 C2 1
7 W 5
+ 7 W 5
______________________________
1 5 U 0
STEP 6: We have middle Column left i.e.
W+W+C1=U
Since C1 =1 W+W must be >9 [to generate carry]
W>=5 To generate carry C2
W can take values 5,6,7,…9
STEP 7: Assume:

Since W+W+C1=U if W=6 then,
6+6+1= 13 Thus U=3 which is Accepted
U=3
STEP 8:
THUS AT THIS STATE SINCE ALL THE VARIABLES HAVE BEEN
ASSIGNED VALUES WHICH COMPLY WITH CONSTRAINTS GIVEN,
WE HAVE REACHED FINAL STATE!!
W=6Assume:
1 1 1
7 6 5
+ 7 6 5
_______________________
1 5 3 0

C R O S S
R O A D S
_______________________________
D A N G E R

Informed and Uninformed search Strategies

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