The document discusses informed search, which applies heuristics to make search algorithms more efficient. A heuristic is a method that estimates the best solution in reasonable time rather than finding the exact best solution. Informed search techniques like best first search use a heuristic function to assign each node a value estimating its distance from the goal. The search prioritizes exploring nodes with lower heuristic values first in the open list in order to find the goal state faster.

2. Informed search
Informed searchis an evolution of
search that applies heuristics to
search algorithm , given the
problem space , to make the
algorithm more efficient.
A heuristic is a method that might
not always find the best solution
but is guranated to find a good
solution is reasonable time.

3. Some info about problem
space(heuristic) is used to compute
preference among the children for
exploration and expansion.
Examples: Best First Search
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.

4. 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.

5. Heuristic Search
Techniques
To idea behind a heuristic search
is that we explore the node that
is most likely to be nearest to a
goal state. To do this we use a
heuristic function which tells us
how close we are to goal state.

6. 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

7. 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.

8. S
B
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OPEN={ S}
CLOSED={}

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

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

11. S
B
A
2
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OPEN={ B(6), H(7),E(8), D(10)}
CLOSED={S,A, C}

12. S
B
A
2
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D
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OPEN={H(7),E(8), D(10), F(13),G(14)}
CLOSED={S,A, C, B}

13. S
B
A
2
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D
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8
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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}

14. S
B
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2
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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}

15. S
B
A
2
13
5
D
E
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8
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F
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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}

16. S
B
A
2
13
5
D
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8
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F
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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

17. 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.

18. THANKS
YOU