It presents the search algorithms in Artificial intelligence

1. Artificial Intelligence
Third Year Students
Department of Computer Science
T.A. Rehab Emad El-Dein Sayed
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2. Agenda
 Search Algorithms in AI
 Search Algorithm Terminologies
 Properties of Search Algorithms
 Types of Search Algorithms
 Search Algorithm over Graph
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3. Search Algorithms in AI
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 Problem-Solving Agents:
 ArtificialIntelligence is the study of buildingagents that act rationally.
 Rational agents or Problem-solving agents in AI mostly used these search
strategies or algorithms to solve a specific problem and provide the best result.
Problem-solving agents are the goal-based agents and use atomic representation.
 Search Algorithms are one of the most important areas of Artificial
Intelligence.
 Search Algorithms are universal problem-solving methods.

4. Agenda
 Search Algorithms in AI
Search Algorithm Terminologies
 Properties of Search Algorithms
 Types of search algorithms
 Search over Graph
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Search Algorithm Terminologies
 Search: Searching is a step by step procedure to solve a search-problem in a given search space.
 A Search Problem can have three main factors:
 Search Space: Search space represents a set of possible solutions, which a system may have.
 Start State: It is a state from where agent begins the search.
 Goal test: It is a function which observe the current state and returns whether the goal state is
achieved or not.
 Search tree: A tree representation of search problem is called Search tree.
 The root of the search tree is the root node which is corresponding to the initial state.
 Actions: It gives the description of all the available actions to the agent.
 Transition model: A description of what each action do, can be represented as a transition model.
 Path Cost: It is a function which assigns a numeric cost to each path.
 Solution: It is an action sequence (Path) which leads from the start node to the goal node.
 Optimal Solution: If a solution has the lowest cost among all solutions.

6. Agenda
 Search Algorithms in AI
 Search Algorithm Terminologies
Properties of Search Algorithms
 Types of Search Algorithms
 Search Algorithm over Graph
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7. Properties of Search Algorithms
 There 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. 7

8. Agenda
 Search Algorithms in AI
 Search Algorithm Terminologies
 Properties of Search Algorithms
Types of Search Algorithms
 Search Algorithm over Graph
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Types of search algorithms
 Based on the search problems we can classify the search algorithms into:
1. Uninformed Search (Blind Search)Algorithms.
2. Informed Search (Heuristic Search) Algorithms.

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Uninformed/BlindSearch:
 Uninformed Search does not contain any domain knowledge such as closeness, the location of the
goal.
 Uninformed Search operates in a brute-force way as it only includes information about how to
traverse the tree and how to identify leaf and goal nodes.
 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.
 Uninformed Search examines each node of the tree until it achieves the goal node.
 It can be divided into 5 main types:
Breadth-first search Iterative deepening depth-first search
Uniform costsearch BidirectionalSearch
Depth-first search
Types of Search Algorithms

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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 strategy 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.
 Informed Search can solve much complex problem which could not be solved in another
way.
 An example of informed search algorithms .
Greedy Search A* Search
Types of Search Algorithms

12. Agenda
 Search Algorithms in AI
 Search Algorithm Terminologies
 Properties of Search Algorithms
 Types of Search Algorithms
Search Algorithm over Graph
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13. Search over Graph
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Python representations of graph components:
 Graph nodes (Vertices): A "graph" in mathematics and computer science consists of "nodes",
also known as "vertices".
 Nodes may or may not be connected with one another.
 the node "a" is connected with the node "c",
 but "a" is not connected with "b".
 Graph edge: The connecting line between two nodes is called an edge.
 Undirected graph: If the edges between the nodes are undirected, the graph is called an
undirected graph.
 Directed graph: If an edge is directed from one vertex (node) to another, a graph is called a
directed graph. An directed edge is called an arc.

14. Usages of Graph in AI
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 Though graphs may look very theoretical, many practical problems can be represented by
graphs.
 They are often used to model problems or situations in physics, biology, psychology and
above all in computer science using graph.
 In computer science, graphs are used to represent networks of communication, data
organization, computational devices, the flow of computation.
 They are used to represent the data organization, like the file system of an operating
system, or communication networks. The link structure of websites can be seen as a graph
as well.

15. Graph Implementation
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 Graph can be implemented in python using data type (dictionary)
 The keys of the dictionaryare the nodes of our graph.
 The values of the dictionary are lists with the nodes, which are connecting by an
edge.
graph = { "a" : ["c"],
"b" : ["c", "e"],
"c" : ["a", "b", "d", "e"],
"d" : ["c"],
"e" : ["c", "b"],
"f" : [] }

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 Declarationof graph Class
 Declarationof Functionsthat generating a list of graph nodes and edges

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Output

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 Declarationof Functionto add a node to graph
 Declarationof Functionto add an edge to graph

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Output

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Output

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Output

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 Declarationof Function__str__ to representgraph

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 Declarationof Function Searchovergraph

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Output

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Assignment
1. Try to update the Method of find_path() to be find_all_paths() finds all the paths
between a start node to an end node.

26. Thanks…
Next: DFS , BFS
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