Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Expert Solution & Answer
Chapter 3, Problem 20E
Explanation of Solution
a.
Vacuum world problem:
- The vacuum world problem can be solved easily using depth-first-search. In this the iterative deepening search is an enhanced version of the depth first search.
- The expansion is used by the iterative deepening search and takes the suboptimal path across nodes before reaching the target node.
- Hence the iterative deepening depth first search is the best search option...
Explanation of Solution
b.
Applying chosen
The computation of the sequence when the top three squares are dirty and agent in centre is as follows,
- Agent: The agent is having the locations of dirty places.
- Goal test: Clean each square so o dirt left in the path...
Explanation of Solution
c.
Constructing and evaluating the performance of a search agent of vacuum world:
- Consider the
- The starting location of the agent id (1,1) and it is facing towards the right.
- The agent can make the following movements – move forward, turn left, turn right, grab dirt or shoot.
- The agent will not have the forward move when the agent reaches the last square, that is when the agent is facing the wall.
- The possibility of moving to the adjacent box is 0.9 for the remaining squares.
- The probability is 0.1 if there is no change in the location. The probability is calculated by assuming that all the components remain static.
- Only the direction changes when a movement is made to turn right or to turn left. The location does not change in this case.
- The probability is 0.8 for the direction. The quarter turn which the agent makes to change the facing direction has a probability of 0
Explanation of Solution
d.
Comparison between the iterative deepening search and the simple random reflex agent:
- While comparing the iterative deepening search with simple random reflex agent, the iterative deepening search agent has the probability of 0.2 in any direction, executes the process.
- The iterative deepening search agent movement can be defined...
Explanation of Solution
e.
Performance of search agent and reflex agent with n:
For N states,
Action: right, left an...
Expert Solution & Answer
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Students have asked these similar questions
Consider the vacuum-world problem defined as shown in the following figure.
a. Which of the algorithms defined in this chapter would be appropriate for this problem? Should the algorithm use tree search or graph search?
b. Apply your chosen algorithm to compute an optimal sequence of actions for a 3×3 world whose initial state has dirt in the three top squares and the agent in the center.
c. Will you prefer an agent with state/ memory in this scenario?
d. Compare your best search agent with a simple randomized reflex agent that sucks if there is dirt and otherwise moves randomly.
e. Consider what would happen if the world were enlarged to n × n. How does the performance of the search agent and of the reflex agent vary with n?
I need these questions solutions as soon as possible sir.
Question: Consider the vacuum-world problem defined as shown in the following figure-1.
a. Which of the algorithms defined in this chapter would be appropriate for this problem? Should the algorithm use tree search or graph search?
b. Apply your chosen algorithm to compute an optimal sequence of actions for a 3×3 world whose initial state has dirt in the three top squares and the agent in the center.
c. Construct a search agent for the vacuum world and evaluate its performance in a set of 3×3 worlds with a probability of 0.2 of dirt in each square. Include the search cost as well as path cost in the performance measure, using a reasonable exchange rate.
d. Compare your best search agent with a simple randomized reflex agent that sucks if there is dirt and otherwise moves randomly.e. Consider what would happen if the world were enlarged to n × n. How does the performance of the search agent and of the reflex agent vary with n?…
I need the algorithm, proof of correctness and runtime analysis for the problem. No code necessary ONLY algorithm. And runtime should be O(n + m) as stated in the question.
Chapter 3 Solutions
Artificial Intelligence: A Modern Approach
Ch. 3 - Explain why problem formulation must follow goal...Ch. 3 - Prob. 2ECh. 3 - Prob. 3ECh. 3 - Prob. 4ECh. 3 - Prob. 5ECh. 3 - Prob. 6ECh. 3 - Prob. 8ECh. 3 - Prob. 9ECh. 3 - Prob. 10ECh. 3 - Prob. 11E
Ch. 3 - Prob. 12ECh. 3 - Prob. 13ECh. 3 - Prob. 14ECh. 3 - Prob. 15ECh. 3 - Prob. 16ECh. 3 - Prob. 17ECh. 3 - Prob. 18ECh. 3 - Prob. 20ECh. 3 - Prob. 21ECh. 3 - Prob. 22ECh. 3 - Trace the operation of A search applied to the...Ch. 3 - Prob. 24ECh. 3 - Prob. 25ECh. 3 - Prob. 26ECh. 3 - Prob. 27ECh. 3 - Prob. 28ECh. 3 - Prob. 29ECh. 3 - Prob. 31ECh. 3 - Prob. 32E
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- I need the algorithm, proof of correctness and runtime analysis for the problem. No code necessary ONLY algorithm. And runtime should be O(n+m) as stated in the question.arrow_forwardthink about a search problem on a graph G = (N, E, C), where N represents nodes, E represents edges between nodes, and the weight of an edge e ∈ E is denoted by C(e), where C(e) > 1 for all e ∈ E. We have a heuristic h that calculates the smallest number of edges from a starting state to a goal state. Now, imagine removing edges from the graph while leaving the heuristic values the same. The question is whether the heuristic remains admissible and consistent after this change. and prove themarrow_forwardVertex S denotes the start state and vertices G1 and G2 denote the two goal states. Directed edges are labelled with the actual costs of traversing the edge. What path from S to a goal would be found by Iterative deepening Depth-First Search? If all else is equal, consider states in alphabetic order. options are : a. S, A, B, C, D, E, F, G1 b. S, A, B, C, E, F, D, G1 c. S, A, B, C, E, F, G1 d. S, A, B, C, G1 e. S, A, C, G1 f. S, A, E, G1 g. S, B, A, C, E, G1 h. S, B, F, D, G2 i. S, C, B, F, A, D, E, G1 j. S, C, G1arrow_forward
- Suppose we have the following undirected graph, and we know that the two bolded edges (B-E and G-E) constitute the global minimum cut of the graph. 1. If we run the Karger’s algorithm for just one time to find the global minimum cut, what is the probability for the algorithm to find the minimum cut correctly? Please show your reasoning process. 2. How many times do we need to run the Karger’s algorithm if we want to guarantee that the probability of success is greater than or equal to 0.95, by “success” we mean that there is at least one time the Karger’s algorithm correctly found the minimum cut. Please show your reasoning process. [You do not have to work out the exact value of a logarithm]arrow_forwardVertex S denotes the start state and vertices G1 and G2 denote the two goal states. Directed edges are labelled with the actual costs of traversing the edge. In what order would Iterative deepening Depth-First Search visit the states when starting the search in S? Consider the sequence of states as they are encountered for the first time on each path in each iteration of the algorithms. Remember that the Depth-First Search utilised in the iterative algorithm forgets that a state has been visited when backtracking over it. If all else is equal, consider states in alphabetic order. Select one: a. S, A, B, C, E, F, G1 b. S, A, C, E, B, A, F, C, G1 c. S, A, C, E, B, F, G1 d. S, A, C, G1 e. S, S, A, B, C, S, A, C, E, B, A, F, C, G1 f. Iterative Deepening Search enters an infinite looparrow_forwardTwo vertices, s and t, are provided together with a directed graph G = (V, E). Additionally, the graph's edges are all either blue or red in color. Finding a road from s to t such that all red edges appear after all blue edges is your objective. The path need not have any red or blue edges, but if it does, all red edges should appear after all blue edges. Create and evaluate an algorithm that will solve this issue in O(n + m) time.arrow_forward
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