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Goal-Based Agents-Pathfinding - Coggle Diagram
Goal-Based Agents-Pathfinding
Types of Searches
Reach the objective, go to each node, and determine the most efficient route there with the least amount of work.
Travelling salesman Problem
Track routing, material management, and gene sequencing are examples of useful applications.
Search Algorithms
BFS-Breadth First Search
Explore each node radiating out from the start
Guarantees shortest path to target
A* Search
An improvement by introducing a heuristic
Each node's cost is equal to the sum of the costs of the edge, the prior node, and the predicted cost to reach the goal.
DFS-Depth First Search
ensures that, in the event that the graph is connected, every node and edge is visited.
Explore the network space thoroughly, follow each node to its limit, then reverse when it comes to a dead end.
Problem Solving-Having Goals
The first step is to formulate goals based on performance metrics and the existing circumstances.
The process of searching for a series of steps that lead to the objective is called search.
Measuring Problem Solving Algorithms Performance
completeness: is a solution guaranteed by the algorithm?
optimality: does the approach identify the best course of action?
Time complexity: The amount of time required to complete the search
Space complexity: The amount of memory required to conduct the search
Diagraph
Having directed edges, where the cost of the edges varies
Nodes specifying a directed edge
Application
traditional search issues, robot navigation, and route planning
Search Strategies
"Uniformed search": No more state-specific information than what is specified in the problem description.
For instance, Depth First Search and Breadth First Search
An informed search is one that is guided by knowledge about the issue.
An example of a greedy search
State Space
State = node, transition = edge
G = {N,E} where N is the set of nodes and E is the linking set of edges.
Historical from representing semantic networks
Each node = integer, each edge connects two nodes
Well-Defined Problem
"Set of Possible Actions": For state s, Actions(s) = { go(Norwich), go(Cardiff), go(Manchester)}
"Transition Model" Result (s, a) - state that results from action a in state s.
"Initial State" in(London)
Example : Result(in(London), go(Norwich) = in(Norwich)
"State space": Network or graph , path = sequence of state connected by a sequence of actions.
Genetic Algorithm
Employs genetics and natural selection concepts.
Main steps include selection, crossover, and mutation.
A search - based algorithm used for solving optimization problems in machine learning
Conclusion
Search in central topic in Artificial Intelligence, essential for problem-solving and achieving goals optimally.
