Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 2, Problem 13E
a.
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Agent program affected
- The agent will continue to suck as the current location remains dirty and it presents no additional challenge.
- Every suck action needs to be replace...
b.
Explanation of Solution
Rational agent design
- The agent must keep touring the squares indefinitely.
- The probability that a square is dirty increases m...
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Students have asked these similar questions
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.
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…
The essence of model-free learning is the policy iteration. Please match the phase and the descriptions/exmples.
a. Policy Evaluation
b. Policy Improvement
Greedily select an action from the Q-Value table
Update Q-Value table
Know how to "good" a policy is.
Make adjustments toward the "good" decision
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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- It uses just condition-action rules where the rules are like the form “if … then …” Goal-based agents Utility-based agents Simple Reflex Agent otherarrow_forwardWrite 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 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_forward
- Frequently, game design and reinforcement learning are lumped together. What are some games that a reinforcement learning agent may easily be trained to solve?arrow_forwardAs we've previously seen, equations describing situations often contain uncertain parameters, that is, parameters that aren't necessarily a single value but instead are associated with a probability distribution function. When more than one of the variables is unknown, the outcome is difficult to visualize. A common way to overcome this difficulty is to simulate the scenario many times and count the number of times different ranges of outcomes occur. One such popular simulation is called a Monte Carlo Simulation. In this problem-solving exercise you will develop a program that will perform a Monte Carlo simulation on a simple profit function. Consider the following total profit function: PT=nPv Where Pr is the total profit, n is the number of vehicles sold and P, is the profit per vehicle. PART A Compute 5 iterations of a Monte Carlo simulation given the following information: n follows a uniform distribution with minimum of 1 and maximum 10 P, follows a normal distribution with a mean…arrow_forwardWrite Algorithm for Steering behaviour rules. Avoidance(A, f ) in: set A of objects to be avoided; boid f out: unit vector indicating avoidance, or zero vector if nothing to avoid constant: avoidance distancearrow_forward
- What is the rule of simple reflex agent? Select one: a. Simple and Condition-action rule b. Simple-action rule O c. None O d. Condition-action rulearrow_forward2. 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…arrow_forwardA robot moves into rooms R1 and R2 and switch the bulbs B1 and B2 on/off. The following are the action schema:1. goto(r, x1, x2) : robot r go to x2 from x12. switchON(s): switchON the bulb s3. switchOFF(s): switchOFF the bulb s 1. Write down preconditions and effects of the above actions. 2. Consider the following: (i) Initial state: < R1;R2;B1;B2 >: Robot is at Room R1 not in Room R2 and both bulbs are off.(ii) Goal state: < R2;B1;B2 >: Robot is at Room R2 and both bulbs are ON.Draw state space diagram for the above by drawing to all possible states.arrow_forward
- Define in your own words the following terms: agent, agent function, agent program, rationality, autonomy, reflex agent, model-based agent, goal-based agent, utility-based agent, learning agent.arrow_forwardIn reinforcement learning, we have to predict an action or value of a state/action, this is like a supervised learning task. What makes reinforcement learning more difficult than classification? Select one: a. It is hard to get samples. b. The supervision is delayed c. There is no supervision in any form. d. It is hard to make a state.arrow_forwardFor each of the following assertions, say whether it is true or false and support your answer with examples or counterexamples where appropriate. An agent that senses only partial information about the state cannot be perfectly rational. b. There exist task environments in which no pure reflex agent can behave rationally. c. There exists a task environment in which every agent is rational. d. The input to an agent program is the same as the input to the agent function. e. Every agent function is implementable by some program/machine combination.arrow_forward
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