The document provides an overview of an introductory course on Artificial Intelligence, covering fundamental concepts such as intelligent agents and various search algorithms, including breadth-first and depth-first search. It outlines properties of search algorithms such as completeness, optimality, time, and space complexity, and differentiates between uninformed and informed search strategies. Additionally, it discusses applications of both breadth-first and depth-first search algorithms in graph traversal and problem-solving contexts.

1. 09/26/2024 Department of CSE (AI/ML) 1
22PCOAM11
INTROUCTION TO ARTIFICAL
INTELLIGENCE (R22 – II (Sem 3)
Department of computer science and
engineering (AIML)
Session 4
by
Asst.Prof.M.Gokilavani
GNITC

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TEXTBOOK:
• Artificial Intelligence A modern Approach, Third
Edition, Stuart Russell and Peter Norvig, Pearson
Education.
REFERENCES:
• Artificial Intelligence, 3rd
Edn, E. Rich and K.Knight
(TMH).
• Artificial Intelligence, 3rd
Edn, Patrick Henny Winston,
Pearson Education.
• Artificial Intelligence, Shivani Goel, Pearson Education.
• Artificial Intelligence and Expert Systems- Patterson,
Pearson Education.
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3. Topics covered in Unit 1
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• Introduction to AI : Intelligent Agent
• Problem-Solving Agents
• Searching for Solutions : Breadth-first search
• Depth-first search
• Hill-climbing search
• Simulated annealing search
• Local Search in Continuous Spaces.

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Properties of Search Algorithms
• Following are the four essential properties of search algorithms to compare the efficiency of
these algorithms:
• Completeness: A search algorithm is said to be complete if it guarantees to return a solution if
at least any solution exists for any random input.
• Optimality: If a solution found for an algorithm is guaranteed to be the best solution (lowest
path cost) among all other solutions, then such a solution for is said to be an optimal solution.
• Time Complexity: Time complexity is a measure of time for an algorithm to complete its task.
• Space Complexity: It is the maximum storage space required at any point during the search, as
the complexity of the problem.
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Types of search algorithms
Based on the search problems we can classify
the search algorithms into
– uninformed (Blind search) search and
– informed search (Heuristic search) algorithms.
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Uninformed/Blind Search
•Uninformed search applies a way in which search tree is searched
without any information about the search space like initial state
operators and test for the goal, so it is also called blind search.
•Don’t have any domain knowledge .
•It examines each node of the tree until it achieves the goal node.
It can be divided into five main types:
– Breadth-first search
– Uniform cost search
– Depth-first search
– Iterative deepening depth-first search
– Bidirectional Search
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Informed Search
• Informed search algorithms use domain knowledge. In an
informed search, problem information is available which can
guide the search.
• Informed search strategies can find a solution more efficiently
than an uninformed search strategy. Informed search is also
called a Heuristic search.
• A heuristic is a way which might not always be guaranteed for
best solutions but guaranteed to find a good solution in
reasonable time.
• An example of informed search algorithms is a traveling
salesman problem.
• Greedy Search
• A* Search
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Breadth-first Search
• Breadth-first search is the most common search strategy
for traversing a tree or graph. This algorithm searches
breadthwise in a tree or graph, so it is called breadth-
first search.
• BFS algorithm starts searching from the root node of the
tree and expands all successor node at the current level
before moving to nodes of next level.
• The breadth-first search algorithm is an example of a
general-graph search algorithm.
• Breadth-first search implemented using FIFO queue
data structure.

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Algorithm
• Step 1: SET STATUS = 1 (ready state) for each node in G
• Step 2: Enqueue the starting node A and set its STATUS =
2 (waiting state)
• Step 3: Repeat Steps 4 and 5 until QUEUE is empty
• Step 4: Dequeue a node N. Process it and set its STATUS
= 3 (processed state).
• Step 5: Enqueue all the neighbours of N that are in the
ready state (whose STATUS = 1) and set
their STATUS = 2
(waiting state)
[END OF LOOP]
• Step 6: EXIT
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Example 1
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• In the example given below, there is a directed
graph having 7 vertices.
• In the above graph, minimum path 'P' can be
found by using the BFS that will start from Node
A and end at Node E.
• The algorithm uses two queues, namely
QUEUE1 and QUEUE2.
• QUEUE1 holds all the nodes that are to be
processed, while QUEUE2 holds all the nodes
that are processed and deleted fromQUEUE1.
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Example 2
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Practice Problem
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Applications of BFS algorithm
• BFS can be used to find the neighboring
locations from a given source location.
• BFS can be used in web crawlers to create web
page indexes.
• BFS is used to determine the shortest path and
minimum spanning tree.
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DFS (Depth First Search) algorithm
• It is a recursive algorithm to search all the vertices of
a tree data structure or a graph.
• The depth-first search (DFS) algorithm starts with the
initial node of graph G and goes deeper until we find
the goal node or the node with no children.
• Because of the recursive nature, stack data structure
can be used to implement the DFS algorithm.
• The process of implementing the DFS is similar to
the BFS algorithm.
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The step by step process to implement the DFS traversal is
given as follows -
• First, create a stack with the total number of vertices in the
graph.
• Now, choose any vertex as the starting point of traversal,
and push that vertex into the stack.
• After that, push a non-visited vertex (adjacent to the vertex
on the top of the stack) to the top of the stack.
• Now, repeat steps 3 and 4 until no vertices are left to visit
from the vertex on the stack's top.
• If no vertex is left, go back and pop a vertex from the stack.
• Repeat steps 2, 3, and 4 until the stack is empty.
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Algorithm
• Step 1: SET STATUS = 1 (ready state) for each node in G
• Step 2: Push the starting node A on the stack and set its STATUS =
2 (waiting state)
• Step 3: Repeat Steps 4 and 5 until STACK is empty
• Step 4: Pop the top node N. Process it and set its STATUS = 3
(processed state)
• Step 5: Push on the stack all the neighbors of N that are in the ready
state (whose STATUS = 1) and set their STATUS = 2 (waiting state)
• [END OF LOOP]
• Step 6: EXIT
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Example
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Applications of DFS
• DFS algorithm can be used to implement the
topological sorting.
• It can be used to find the paths between two
vertices.
• It can also be used to detect cycles in the graph.
• DFS algorithm is also used for one solution
puzzles.
• DFS is used to determine if a graph is bipartite or
not.
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Topics to be covered in next session 5
• Hill climbing Search
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Thank you!!!