Consider navigating the maze shown below (same maze used in the homework).(NM42K222EBG22FH2The maze is represented as a graph with edge costs as shown on the edges. The edge cost is 1 for all edges where the cost is not shown. Let B be the initial state and G is the goal state. The heuristic cost of every node to reach G is :h(n)ThA5.1B4.1C3.9D14E2.2IF3.8G0IH3.7IJ7IK16L14IM0.5IN1.5IP1.8S4.5Show the steps of an A* search starting from B to goal G:.Show the frontier and explored set in every iteration (i.e., complete the above table)List the vertices in the order they are expanded.

In computer science and artificial intelligence, the A* search algorithm is a well-liked and…

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Please implement the A* search algorithm in Python for the problem scenario below. Please explain your code and your approach. PROBLEM SCENARIO On holiday, a flight currently wants to travel to Bucharest from Arad. But there isno direct way to Bucharest from Arad. However, the cities are connected witheach other like a graph. The distance between the connected cities are given. Theflight wants to travel through the most optimal way. To find the optimal path totravel, another information is provided: the straight line distance between any cityand the final destination (Bucharest). Now apply A* search to determine the most optimal value for the route Arad toBucharest and help the flight. You have to use the straight line distance as theheuristic value for the cities. City Heuristic value City Heuristic valueArad 366 Mehadia 241Bucharest 0 Neamt 234Craiova 160 Oradea 380Eforie…
The clique problem is finding cliques in a diagram. A clique is a set of vertices that are adjacent to each other. The 4-clique is a set of four knots all connected. So, in this example of the 4-clique problem, we have a graph with 7 vertices. A brute force algorithm searched all possible combinations of four vertices and found a set that formed a clique. If you want to understand more about it, the problem (and if possible read on). Note that the clique problem is NP-complete, so deterministic search is not practical for large graph sizes. This makes it an ideal candidate for evolutionary exploration. In this problem, we have to assume that we are given the problem of implementing the 4-clique problem as an evolutionary algorithm for an arbitrary graph with an arbitrary number of vertices (an n-vertex graph). If 4 cliques are found, the algorithm succeeds. 1. Provide an algebraic expression, in terms of n, for the size of the phenotypic search space (the number of possible…
The given inputs consist of two nodes (s, t) and a directed graph G = (V, E). In addition, each edge of the graph is either blue or red. The goal is to find a path from point s to point t such that red edges always follow blue edges. There need not be any red or blue borders on the route, but if there are, the red ones should follow the blue ones. Develop an algorithm that does the task in O(n + m) time and analyze its performance.
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There are many applications of Shortest Path Algorithm. Consider the problem of solving a jumbled Rubik's Cube in the fewest number of moves. I claim that this problem can be solved using a Shortest Path Algorithm. Determine whether this statement is TRUE or FALSE. NOTE: if you want to check if this statement is TRUE, think about how the Rubik's Cube Problem can be represented as a graph. What are the vertices? Which pairs of vertices are connected with edges? What is your source vertex and what is your destination vertex? How would Dijkstra's Algorithm enable you to find the optimal sequence of moves to solve a jumbled cube in the fewest number of moves?
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