Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search, Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A * algorithm, Iterative Deepening A* , Recursive Best First Search, Pruning the CLOSED and OPEN Lists

1. Topic To Be Covered:
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE
II. HEURISTIC FUNCTION IN AI
III. BEST FIRST SEARCH IN AI
Jagdamba Education Society's
SND College of Engineering & Research Centre
Department of Computer Engineering
SUBJECT: Artificial Intelligence & Robotics
Lecture No-08
Prof.Dhakane Vikas N

2. Types of search in ai
II. Informed Search In AI
 It is search with information.
 It is greedy search method
 Use knowledge to find steps to solution.
 Less Complexity(Time, Space)
 Quick Solution
 Know about Start state and Goal state & Also
know how to reach.
 informed search algorithm contains an
array of knowledge such as how far we are
from the goal, path cost, how to reach to
goal node, etc. This knowledge help agents
to explore less to the search space and find
more efficiently the goal node.
 The informed search algorithm is more
useful for large search space. Informed
search algorithm uses the idea of
heuristic, so it is also called Heuristic
search.

3. HEURISTIC FUNCTION in ai
 Heuristic is a function which is used in
Informed Search, and it finds the most
promising path.
 It takes the current state of the agent as its
input and produces the estimation of how
close agent is from the goal.
 Heuristic function estimates how close a
state is to the goal. It is represented by
h(n), and it calculates the cost of an
optimal path between the pair of states.
 The heuristic method, however, might not
always give the best solution, but it
guaranteed to find a good solution in
reasonable time.
 This technique always use to find solution
quickly.

4. HEURISTIC FUNCTION in ai
 Heuristic is a function which is
used in Informed Search, and it
finds the most promising path.
 It takes the current state of the
agent as its input and produces
the estimation of how close
agent is from the goal.
 Heuristic function estimates
how close a state is to the goal.
It is represented by h(n), and it
calculates the cost of an
optimal path between the pair
of states.
 The heuristic method,
however, might not always
give the best solution, but it
guaranteed to find a good
solution in reasonable time.

5. HEURISTIC FUNCTION in ai
Different Method Used To Estimate Heuristic Value
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I. Euclidian Distance (Straight Line Distance Method)
 Euclidean distance is the distance between two points
in Euclidean space. Euclidean space was originally devised by the Greek
mathematician Euclid around 300 B.C.E. to study the relationships
between angles and distances.

6. HEURISTIC FUNCTION in ai
Different Method Used To
Estimate Heuristic Value
II. Manhattan Distance
 In case of 8-Puzzle problem
Manhattan distances are
nothing but Number of
moves need to be made by AI
agent so that it can reach to
its goal state.

7. HEURISTIC FUNCTION in ai
Different Method Used
To Estimate Heuristic
Value
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III. No.of Misplaced
Tiles

8. BEST FIRST SEARCH(BFS)
 This is informed search technique
also called as HEURISTIC search.
 This algo. Works using heuristic
value.
 This algorithm uses evaluation
function to decide which adjacent
node is most promising and then
explore.
 Priority queue is used to store cost
of function.
 Space &Time Complexity of BFS is
also O(V+E) whereV is vertices and E
is edges.
 Also Written as:-O(b) ^d
Where, b->Branching factor
d->depth

9. BEST FIRST SEARCH(BFS)
Algorithm
 Priority queue ‘PQ’ containing
initial states
Loop
 If PQ=Empty Return Fail
Else
 NODE<-Remove_First(PQ)
 If NODE=GOAL
 Return path from initial state to
NODE
ELSE
 Generate all successor of NODE
and insert newly generated NODE
into ‘PQ’ according to cost value.
END LOOP

10. BEST FIRST SEARCH(BFS)
Advantages of BFS:
Memory efficient as compared with DFS & BFS
It is Complete
Disadvantages of BFS:
It gives good solution but not optimal solution.
In worst case it may behave like unguided DFS