Depth-First Tree Search - Iterative Deepening Search - Minimum Remaining Values (MRV) - IT/Computer Science Assignment Help

Assignment Task:

a. Learning real-time A* (LRTA*) it builds a map of the environment. It updates the cost estimate for the state it has just left and then chooses the “apparently best” move according to its current cost estimates. Assume that an agent using LRTA* if it is on state S what is the next state? (Cost of operators assigned in the arc and Estimated value to the goal is in each circle) It evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost

to get from the node to the goal: 

f (n) = g(n) + h(n) .

b.  “S” is the start node, and “G” is the only goal node. The number next to each arc is the operator cost for that arc.

Write the order in which nodes are expanded for each type of search:

b1.  Iterative deepening search (or iterative deepening depth-first search) is a general strategy,often used in combination with depth-first tree search, that finds the best depth limit. 

Note : Assume that children of a node are returned in alphabetical order whenever the node is expanded

b2. “S” is the start node, and “G” is the only goal node. In the table we provide the estimate of the remaining distance to the goal node “G”. The number next to each arc is the operator cost for that arc. 

The most widely known form of best-first search is called A* search (pronounced “A-star search”). It evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost to get from the node to the goal:

f (n) = g(n) + h(n) .

c. The following problem asks about MINI-MAX search in game trees. Following the tree use Alpha-Beta Pruning. In this game Max Player will start the game. Please fill the Alpha and Beta in each step of. As example :  

α = -inf , 7 , 2

β = inf , 11 , 1

Indicate which nodes will be pruned in this game.

d. Imagine the graph is representing the border between cities. We have three colors {Red, Green, Blue}. Coloring this map can be viewed as a Constraint Satisfaction Problem (CSP). The goal is to assign colors to each city so that no neighboring cities have the same color. 

Assumptions:

1. Consider that we are solving this problem with Backtracking

2. To choose the next move we use Minimum Remaining Values (MRV) heuristic (fewest legal values)

3. Start from SA with color Red.

If we start from SA, Then the next level to explore will be WA, NS, V nodes. Based on our heuristic (Assumption two) which one will be expanded first please justify your choice (Recommended to draw a tree that shows the possible movements) 

This IT/Computer Science Assignment has been solved by our IT/Computer Science Experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.