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For Example: Expansion Order Cost 1 Cost(A) = 78 Node Name A Take h(n) = number of misplaced tiles. Initial State Goal State 2 8 3 1 4 7 6 5 1 2 3 8 4 7 6 5 h(n) = 3 

          For Example:
Expansion
Order
Cost
1
Cost(A) = 78
Node Name
A
Take h(n) = number of misplaced tiles.
Initial State
Goal State
2 8 3
1 4
7 6 5
1 2 3
8 4
7 6 5
h(n) = 3
For Example: Expansion Order Cost 1 Cost(A) = 78 Node Name A Take h(n) = number of misplaced tiles. Initial State Goal State 2 8 3 1 4 7 6 5 1 2 3 8 4 7 6 5 h(n) = 3

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Computer Science and Information Technology
Computer Science and Information Technology
Trishna Knowledge Systems 2018 Edition
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In the following 8-puzzle problem, the initial state, the goal state, and the cost function are given below. Find the actions (which states) to reach the goal state using the A* Search algorithm. In your solution: - Draw the search tree (30%) - Write the cost at each state (30%) - Write a number at each state showing its expansion order (i.e. the root's number will be 1) (30%) - Write the solution path (10%) SHOW THE EXECUTION OF THE ALGORITHMS STEP BY STEP. Assume that the algorithms keep an explored set to handle repeated states. Use the following labeling method to show the expansion order and cost at each node. For Example: Cost Cost(A) = 78 Node Name A Take h(n) = number of misplaced tiles. Initial State 2 8 1 7 6 3 4 5 h(n)=3 Goal State 2 3 4 8 6 5 /
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Transcript

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00:01 In this question, let's analyze the search engine strategies.
00:14 So that is 3 best first search.
00:20 Next one is depth first search and third one is uniform cost search.
00:49 Okay.
00:49 So now we will write the directed graph as required.
00:57 So graph.
01:02 Yes, 8 8 9 3 3 1 d 11 5 g 3 so c 10 2 0 8 6 4 0 g 3 1 f g 2 5 e 0 g 1.
01:54 So now let's see the greedy best first search best for search through the following steps.
02:14 So start from initial state.
02:17 Yes, second step expand the node with the lowest estimated cost to the nearest goal state third one repeat until the goal state is reached for all the nodes are explored nodes are explored.
02:55 Okay.
02:56 Now, let's first trace the steps.
03:07 So for s of cost 8 a of cost 9 c of cost 3 e of cost 0 next one is solution path start state.
03:45 Yes, he is the goal.
03:50 Right? so next let's see the third step total cost to the goal is 8 plus 9 plus 3 plus 0 is equal to 20.
04:13 So next we will see the fourth step that is guaranteed optimal solution optimal solution.
04:41 So here no greedy best search does not guarantee an optimal solution.
04:47 It chooses the next node based on the heuristic that is estimated cost to the goal cost to the goal without considering the actual path cost.
05:09 So next fifth point is time and space complexity.
05:14 So here time complexity depends on the structure of the graph, but in the worst case it can be exponential that is omega p power d where b is the branching factor and d is the depth of the optimal solution space complexity.
06:03 That is omega p power d where b is the branching factor and d is the maximum depth of the search tree.
06:19 So next we will see the depth first search depth for search.
06:39 Okay in this it has same points as staff and the initial state is next point explore as far as possible along each branch before backtracking backtracking repeat until a goal state is reached.
07:15 So here also there are steps to do.
07:17 So first step is trace of steps trace of steps here...
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