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
Explanation of Solution
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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Develop a simulation that shows the actions you can run an intelligent agent on a System based on the learned knowledge. For this model you must use a language high-level programming.
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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Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.Similar questions
- 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_forwardWrite PEAS descriptions for the following tasks. i. A search and rescue robot for exploring a collapsed mine. ii. An agent for face detection.arrow_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_forward
- In 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_forward3. Please write in complete sentences The trap alarm thresholds are set at two levels—rising and falling. Explain the reasoning behind this.arrow_forwardWrite an experiment protocol for calculating accuracy difference between touchpad pointing and mouse pointing using Laptop. You have 5 users available to perform experiment tasks. You are required to provide rationale for all your decisions. The experiment protocol should at least describe the following elements: -> Hypothesis -> Independent Variables -> Dependent Variables -> Subject Design/Group Design -> Experiment Procedurearrow_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_forwardDraw a sequence diagram for the HappyHourMessage scenario in the figure below. Draw only the first five messages exchanged.arrow_forward
- Let’s say, you are given a task to identify the community which is severely infected by a virus such as Covid-19. As an environmental engineer, using the concepts of conditional probability, how can you develop a mathematical model to identify the infected community. Write all the parameters and show their relationships?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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