- The document summarizes topics covered in an Artificial Intelligence session on problem solving by search algorithms. It discusses uniform cost search, iterative deepening depth-first search (IDDFS), and bidirectional search. For each algorithm, it provides a brief definition and discusses advantages and disadvantages. The session covered uninformed and informed search strategies, including greedy best-first search and A* search.

1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 6
by
Asst.Prof.M.Gokilavani
VITS
2/23/2023 Department of CSE (AI/ML) 1

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 6
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• Problem solving by search-I: Introduction to AI, Intelligent
Agents.
• Problem solving by search-II: Problem solving agents,
searching for solutions
• Uniformed search strategies: BFS, Uniform cost search,
DFS, Iterative deepening Depth-first search, Bidirectional
search,
• Informed ( Heuristic) search strategies: Greedy best-first
search, A* search, Heuristic functions
• Beyond classical search: Hill- climbing Search, Simulated
annealing search, Local search in continuous spaces, Searching
with non-deterministic Actions, searching with partial
observations, online search agents and unknown environments.

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 7
• Informed (Heuristic) Search Strategies
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Thank you!!!