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.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-09(I)
Prof.Dhakane Vikas N

2. BEST FIRST SEARCH(BFS)
 This is informed search technique(Greedy Search) 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) where V is vertices and
E is edges.
 Also Written as:-O(b) ^d
Where, b->Branching factor
d->depth

3. BEST FIRST SEARCH(BFS)
 The implementation of Best
First Search Algorithm involves
maintaining two lists- OPEN and
CLOSED.
 OPEN contains those nodes that
have been evaluated by the
heuristic function but have not
been expanded into successors
yet.
 CLOSED contains those nodes
that have already been visited.

4. BEST FIRST SEARCH(BFS)
Algorithm
 Priority queue ‘PQ’ containing
initial states
Loop
 If PQ=Empty Return Fail
Else
 Insert Node into PQ(Open-List)
 Remove First(PQ) ->NODE(Close-
List)<
 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

5. 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