The document outlines the content of a session on artificial intelligence, specifically focusing on various search algorithms such as uniform-cost search, iterative deepening depth-first search, and bidirectional search. It includes advantages and disadvantages of each algorithm, information about their implementation, and transitions to future topics. Lastly, it references key textbooks and materials relevant to the course.

1. AI3391 ARTIFICAL INTELLIGENCE
(II YEAR (III Sem))
Department of Artificial Intelligence and
Data Science
Session 8
by
Asst.Prof.M.Gokilavani
NIET
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2. 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 session 8
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Unit I: Intelligent Agent
• Introduction to AI
• Agents and Environments
• Concept of Rationality
• Nature of environment
• Structure of Agents
• Problem solving agents
• Search Algorithm
• Uniform search Algorithm

4. Uniform cost search
• Uniform-cost search is a searching algorithm used for
traversing a weighted tree or graph.
• The primary goal of the uniform-cost search is to find a path
to the goal node which has the lowest cumulative cost.
• Uniform-cost search expands nodes according to their path
costs form the root node.
• It can be used to solve any graph/tree where the optimal
cost is in demand.
• A uniform-cost search algorithm is implemented by the
priority queue.
• It gives maximum priority to the lowest cumulative cost.
• Uniform cost search is equivalent to BFS algorithm if the
path cost of all edges is the same.
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6. • Advantages:
– Uniform cost search is optimal because at every
state the path with the least cost is chosen.
• Disadvantages:
– It does not care about the number of steps involve
in searching and only concerned about path cost.
Due to which this algorithm may be stuck in an
infinite loop.
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7. Iterative Deepening Depth First
Search (IDDFS)
• It is a search algorithm that uses the combined
power of the BFS and DFS Algorithm.
• It is iterative in nature.
• It searches for the best depth in each iteration.
It performs the Algorithm until it reaches the
goal node.
• The algorithm is set to search until a certain
depth and the depth keeps increasing at every
iteration until it reaches the goal state.
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10. • Advantages:
– It combines the benefits of BFS and DFS search
algorithm in terms of fast search and memory
efficiency.
• Disadvantages:
– The main drawback of IDDFS is that it repeats all
the work of the previous phase.
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11. Bidirectional Search Algorithm
• Before moving into bidirectional search let’s first
understand a few terms.
• Forward Search: Looking in-front of the end from
start.
• Backward Search: Looking from end to the start back-
wards.
• Bidirectional search replaces one single search graph
with two small sub graphs in which one starts the
search from an initial vertex and other starts from goal
vertex.
• The search stops when these two graphs intersect each
other.
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13. • This algorithm divides one graph/tree into two
sub-graphs.
• It starts traversing from node 1 in the forward
direction and starts from goal node 16 in the
backward direction.
• The algorithm terminates at node 9 where two
searches meet.
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14. Bidirectional search can use search techniques
such as BFS, DFS, DLS, etc.
Advantages:
– Bidirectional search is fast.
– Bidirectional search requires less memory
Disadvantages:
– Implementation of the bidirectional search tree is
difficult.
– In bidirectional search, one should know the goal
state in advance.
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15. Topics to be covered in next session 9
• Informed (Heuristic) Search Strategies
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Thank you!!!