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Transcribed image text: Question 1 (30%) Chongqing Guangzhou Chongqing 562 0 860 610 545 Guilin 294 312 Guangzhou Wuhan Straight line distance from Guangzhou Hong Kong Changsha Xiamen 218 Changsha 412 114 105 400 400 Wuhan 224 230 Nanchang 427 Hong Kong 646 384 Guilin Nanchang Xiam 280 485 (a) Find the shortest path from Chongqing to Xiamen using Depth-First Search. Show all intermediate search trees. (b) Find the shortest path from Guangzhou to Wuhan using Recursive Best-First Search. Show all intermediate search trees. (c) Find the shortest path from Guilin to Xiamen using Iterative Deepening Depth-First Search. Show all intermediate search trees. (d) Describe how to use the Simulated Annealing Search to solve an optimization problem.

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Answer:

Answer:

(a) To find the shortest path from Chongqing to Xiamen using Depth-First Search, we can use the following algorithm:

Start from the Chongqing node and mark it as visited

Visit one of its neighbors (say, Guilin) that has not been visited yet and mark it as visited

Repeat the above step for the new node (Guilin), visiting an unvisited neighbor (Wuhan)

Continue this process until the goal node (Xiamen) is reached or until all nodes have been visited

If the goal node is found, return the path from the start to the goal node. If no path is found, return "no path"

The intermediate search trees are shown below:

Search tree after visiting Chongqing: Chongqing

Search tree after visiting Guilin: Chongqing | Guilin

Search tree after visiting Wuhan: Chongqing | Guilin--Wuhan

Search tree after visiting Nanchang: Chongqing | Guilin--Wuhan | Nanchang

Search tree after visiting Xiamen (goal node): Chongqing | Guilin--Wuhan | Nanchang--Xiamen

So the shortest path from Chongqing to Xiamen using Depth-First Search is: Chongqing -> Guilin -> Wuhan -> Nanchang -> Xiamen.

(b) To find the shortest path from Guangzhou to Wuhan using Recursive Best-First Search, we can use the following algorithm:

Start from the Guangzhou node and calculate the heuristic value (estimated distance) to the goal node (Wuhan)

Add the start node to the open list and mark it as visited

While the open list is not empty:

Get the node with the lowest f-value (heuristic + actual distance) from the open list

If this node is the goal node, return the path from the start to the goal node

Otherwise, expand the node by generating its unvisited neighbors and calculating their f-values

Add these neighbors to the open list and mark them as visited

Update the f-values of any neighbors already on the open list if a better path is found

The intermediate search trees are shown below:

Search tree after visiting Guangzhou: Guangzhou

Search tree after visiting Wuhan (goal node): Guangzhou--Wuhan

So the shortest path from

Step-by-step explanation:

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