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8. Find the solution for following map coloring problem using back tracking. Draw the constraint graph. Explain the terms constraint propagation and forward checking using the same. Northern Territory Queensland Western Australia South Australia New South Wales Victoria

          8. Find the solution for following map coloring problem using back tracking. Draw the constraint graph. Explain the terms constraint propagation and forward checking using the same.
Northern
Territory
Queensland
Western Australia
South
Australia
New South
Wales
Victoria
        
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8. Find the solution for following map coloring problem using back tracking. Draw the constraint graph. Explain the terms constraint propagation and forward checking using the same.
Northern
Territory
Queensland
Western Australia
South
Australia
New South
Wales
Victoria

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Computer Science and Information Technology
Computer Science and Information Technology
Trishna Knowledge Systems 2018 Edition
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Find the solution for the following map coloring problem using backtracking. Draw the constraint graph. Explain the terms constraint propagation and forward checking using the same. Northern Territory Queensland South Australia New South Wales
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Transcript

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00:01 Hello student here the constraint satisfaction problem which is csp graph representation graph is given to us vertices represent the variable in the csp.
00:09 So in this case the vertices represent the different nodes in the graph edges, links or the neighboring function represent the constraint between the variables.
00:17 In this case the edges exist between the two nodes if two nodes are adjacent in the graph this means that the two nodes cannot be assigned the same color.
00:25 So here adjacent node cannot be assigned the same color.
00:30 So here if i am painting it red so it cannot be red it cannot be red.
00:34 So csp method most constraint variable or minimum remaining value mrv heuristic for the variable selections.
00:43 So here mrv heuristic for the variable selection chooses the variable with the fewest remaining possible values.
00:50 So this heuristic can be used to prune the searching space and find a solution more quickly.
00:56 So with the help of this we can do pruning or cutting the search space and find a solution more quickly.
01:04 Example suppose node 1 top is colored blue and the remaining node in the graph have the following number of the remaining possible values.
01:13 So node 2 if we have blue red and green color then node 1 is painted blue color then it's adjacent node 2 and node 3.
01:24 So here it will be either are red or green and here node 3 rgb.
01:32 So here three color options must look at the graph the graph is given to us.
01:37 So here this is node 1.
01:39 So here if i take it's adjacent node so i find with the three options so three options and node 4 only the one options.
01:50 So according to the mrv we should choose the node 4 to color next.
01:54 So least constraint values for the value ordering least constraining value heuristic for the value opening choose the value that eliminate the most constraint this heuristic can also be used to prune the search space tree to find the solution quickly.
02:09 Example suppose we are coloring node 4 we have the following three possible values.
02:14 So if eliminate r or if we are taking r eliminate zero constraint if we are taking g eliminate one constraint and if we are taking b eliminate the two constraint according to the least constraint value heuristic we should choose the value b...
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