Artificial Intelligence The outer loop of iterative widening gradually increases

Question

Artificial Intelligence

The outer loop of iterative widening gradually increases the bound $W$ up to a given $WMax$.
The inner loop has the same skeleton as a standard graph search, with the exception that non-novel states are immediately thrown away.
For now, implement iterative widening's inner loop using breadth-first search.

Your search should return the sequence of actions required to reach a goal condition from an initial condition, along with the cost of that plan.
You also may want to print output describing how many nodes are visited and how much time has been taken for each value of $W$.

Iterated Widening is a pruning technique that admits a lot of different search techniques:
Try it with:
* Depth-First Search
    * Try changing the order of the recipes
        * the default order
        * randomly shuffled
        * sorted from lowest to highest cost
        * highest to lowest cost
* Breadth-First Search

GIVEN:

Explanation / Answer

* DFS has the ability to keep track of the nodes that have already been visited.Foloowing pseudocode allow this festure of DFS:

DFS(U):

visit(u);

time = time+1;

d[u] = time;

color[u] = grey;

for all nodes v adjacent to u do

if color[v] == white then

' p[v] = u;

DFS(u);

time = time+!;

f[u] = time;

color[u] = black;

* BFS is a method in which the neighbours of the node will be visited in the order in which they were inserted in the node which is explained as follows:

BFS(G,s)

let Q be queue:

Q.enqueue(s)

mark s as visited.

while (Q is not empty)

v = Q.dequeue()

for all neighbours w of v in graph G

if w is not visited

Q.enqueue(w)

mark w as visited.

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