Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 2, Problem 12E
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Behaviour of agent
- The main difference is instead of using the location percept to build the map, the agent has to invent its own locations...
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Simulation Assignment IV
Simulate the token ring problem to obtain the average time the token goes around and it confidence
interval (i.e., going around means token starts at a station of your choice and comes back to the same
station).
PASTE TEXT CODE HERE
PASTE THE PRINTOUT OF A PART OF YOUR EVENT TABLE (15 TO 20 EVENTS) IN HERE (IN TEXT OR IMAGE
FILE)
WHAT IS THE AVERAGE TIME TOKEN GOES AROUND AND ITS CONFIDENCE INTERVAL.
2. Suppose that an agent is in a 3×3 maze environment like the one shown in Figure 4.19.
The agent knows that its initial location is (1,1), that the goal is at (3,3), and that the four
actions *Up*, *Down*, *Left*, *Right* have their usual effects unless blocked by a wall.
The agent does *not* know where the internal walls are. In any given state, the agent
perceives the set of legal actions; it can also tell whether the state is one it has visited
before or is a new state.
a. Explain how this online search problem can be viewed as an offline search in belief‐state space, where the initial belief state includes all possible environment configurations. How large is the initial belief state? How large is the space of belief states?
b. How many distinct percepts are possible in the initial state?
c. Describe the first few branches of a contingency plan for this problem. How large
(roughly) is the complete plan?
Notice that this contingency plan is a solution for *every possible…
Write a Java program to simulate the behaviour of a model-based agent for a vacuum cleaner environment based on the following conditions:
The vacuum cleaner can move to one of 4 squares: A, B, C, or D as shown in Table 1.
Table 1: vacuum cleaner environment
A
B
C
D
The vacuum cleaner checks the status of all squares and takes action based on the following order:
If all squares are clean, the vacuum cleaner stays in its current location.
If the current location is not clean, the vacuum cleaner stays in its current location to clean it up.
The vacuum cleaner can only move horizontally or vertically (cannot move diagonally).
The vacuum cleaner moves only one square at a time.
Horizontal moves have the highest priority over vertical moves.
The vacuum cleaner moves to another square only when it needs to be cleaned up. If a diagonal square needs to be cleaned up, the vacuum cleaner moves to its neighbour vertical square first.
The vacuum cleaner action is…
Chapter 2 Solutions
Artificial Intelligence: A Modern Approach
Ch. 2 - Suppose that the performance measure is concerned...Ch. 2 - Let us examine the rationality of various...Ch. 2 - Prob. 3ECh. 2 - For each of the following activities, give a PEAS...Ch. 2 - Define in your own words the following terms:...Ch. 2 - Prob. 6ECh. 2 - Prob. 7ECh. 2 - Implement a performance-measuring environment...Ch. 2 - Prob. 9ECh. 2 - Prob. 10E
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- Write a Java program to simulate the behaviour of a model-based agent for a vacuum cleaner environment based on the following conditions: The vacuum cleaner can move to one of 4 squares: A, B, C, or D as shown in Table 1. Table 1: vacuum cleaner environment A B C D The vacuum cleaner checks the status of all squares and takes action based on the following order: If all squares are clean, the vacuum cleaner stays in its current location. If the current location is not clean, the vacuum cleaner stays in its current location to clean it up. The vacuum cleaner can only move horizontally or vertically (cannot move diagonally). The vacuum cleaner moves only one square at a time. Horizontal moves have the highest priority over vertical moves. The vacuum cleaner moves to another square only when it needs to be cleaned up. If a diagonal square needs to be cleaned up, the vacuum cleaner moves to its neighbour vertical square first. The vacuum cleaner action is…arrow_forwardConsider the case when several persons play the wireless phone game. They send a message from the first person until the last one and the message gets distorted. Simulate this process according to your own distortion rules.arrow_forwardIn attached image, there are 5 states, a, b, c, d, e. Two actions are available for each state: East, West except for the exit states a and e, where the only action available is “Exit”. The transition is deterministic. The rewards of the exit states are given as shown in Image. A) For γ= 1, what is the true utility ? (Please fill the form completely) Example response format: 10 10 10 10 10 (Please note the space!) B) For γ = 0.1, what is the true utility? Example response format: 10 0.1 10 10 0.1 (Please pay attention to the space!) C) For which γ are West and East equally good at state d? (please take to the fourth decimal place)Example response format: γ = 0.1234 (take to the fourth decimal place, please pay attention to the space!)arrow_forward
- Implement the below agent program in Python language (simply). Attach the output as wellarrow_forwardWrite Algorithm for Steering behaviour rules. Avoidance(A, f )in: set A of objects to be avoided; boid fout: unit vector indicating avoidance, or zero vector if nothing to avoidconstant: avoidance distance daarrow_forwardIn Task 2, you try to look inside the generative process. What can you say about it? Select one: a. The generative process appears slightly chaotic and is not immediately clear for a human to interpret. b. The model starts with the image it came up with which is first in black and white. It then gradually colours the image one bit at a time. c. The model drafts things similarly as a human would. It starts by sketching out outlines and then fills in the details.arrow_forward
- Ten participants, who were experienced game players, took part in theexperiment. During the experiment sensors were placed on the participants to collect physiological data. These included measures of the moisture produced by sweat glands in the hands and feet, and changes in heart rate and breathing rate. In addition, they videoed participants and asked them to complete user satisfaction questionnaires at the end of the experiment. In order to reduce the effects of learning, half of the participants played first against a friend and then against the computer, and the other half played against the computer first. the setup for recording data while the participants were playing the game. The display shows the physiological data (top right), two participants, and a screen of the game they played. Results from the user satisfaction questionnaire revealed that the mean ratings on a 1–5 scale for each item indicated that playing against a friend was the favored experience in tabular form.…arrow_forwardWrite a Java program to simulate the behavior of a model-based agent for a vacuum cleaner environment based on the following conditions: The vacuum cleaner can move to one of 4 squares: A, B, C, or D as shown in Table 1. Table 1: vacuum cleaner environment A B C D The vacuum cleaner checks the status of all squares and takes action based on the following order: If all squares are clean, the vacuum cleaner stays in its current location. If the current location is not clean, the vacuum cleaner stays in its current location to clean it up. The vacuum cleaner can only move horizontally or vertically (cannot move diagonally). The vacuum cleaner moves only one square at a time. Horizontal moves have the highest priority over vertical moves. The vacuum cleaner moves to another square only when it needs to be cleaned up. If a diagonal square needs to be cleaned up, the vacuum cleaner moves to its neighbor vertical square first. The vacuum cleaner action is…arrow_forwardImagine an agent like the one in the interactive demo. This agent has a value function like the following, and always chooses the action with the highest value: Q(s=near berry with empty stomach, a=eat berry) = 15Q(s=near berry with empty stomach, a=turn around) = -4Q(s=near berry with berry in stomach, a=eat berry) = 8Q(s=near berry with berry in stomach, a=turn around) = 4Q(s=not near berry with empty stomach, a=look for berry) = 1 I am taking cognitive science, and I have come across this question regarding Q-Learning. I don't understand the question nor do I understand how to do it. Can anyone help? Here is the question below: The agent's eyes detect two berries in front of the agent. Each berry is one step away from the agent (one berry is to the northwest of the agent, the other berry is to the northeast of the agent). The agent eats one of the berries, and experiences a reward of 9. The agent then updates its value function based on this experience using Q-Learning. If…arrow_forward
- Make the exact copy of this graph with the correct placement of the oral and blue circles and with the two lines that are overlapping eachother and going across the graph. Make it on MATLAB. Send the code of this graph. Please make sure they are place the circles and lines exactly the same color and same place. Please make it exactly the same. I do not have any data to help you place the circle and the line. Just try your best on making the exact copy. Please send the code I need help with this.arrow_forwardAn agent can learn to play chess by supervised learning-by being given examples of game situations along with the best moves for those situations. But there is no friendly teacher providing examples. What can the agent do in such situationarrow_forwardSupervised learning example: Iris classificationLet’s take a look at another example of this process, using the Iris dataset we discussed earlier. Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?arrow_forward
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