Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 2, Problem 10E
a.
Explanation of Solution
Simple reflex agent being rational
“No”. A simple reflex agent can’t be rational.
Reason
- A simple reflex agent can be partially observable, stochastic and rational...
b.
Explanation of Solution
Reflex agent with state
“No”. A simple reflex agent with state can’t be rational.
Reason
- A simple reflex agent can be partially observable, stochastic and rational...
c.
Explanation of Solution
Agent’s percept gives clean status
- A simple reflex agent can be perfectly rational.
- The agent can consist of a table with eight entries indexed by percept that specifies an action to take for each possible state...
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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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- Consider a robot that is capable of picking objects from a shelf and placing them on the table. Suppose that the robot’s arm works perfectly, and the environment is fully-observable. (i) Define a STRIPS operator that could be used for planning the actions of this robot. (ii) Give an example of a state S of the world at which this STRIPS operator is applicable. (iii) Describe the operation of this STRIPS operator at this state S, to show the next state S0 of the world.arrow_forwardGive the initial state, goal test, actions/operators, and path cost for the following description. There are several possible formulations for the problem, with varying levels of detail. Pay special attention to different path costs for various actions, and conditions for when those actions are valid. The main thing is that your formulation should be precise and “hang together” so that it could actually be implemented. A monkey is in a room with a crate, with bananas suspended just out of reach on the ceiling. He would like to get the bananasarrow_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_forward
- Consider the problem of learning the target concept "pairs of people who live in the same house," denoted by the predicate HouseMates(x, y). Below is a positive example of the concept. HouseMates (Joe, Sue) Person(Joe) Person(Sue) Sex(Joe, Male) Sex(Sue, Female) Hair Color (Joe, Black) Haircolor (Sue, Brown) Height ( Joe, Short) Height (Sue, Short) Nationality (Joe, US) Nationality (Sue, US) Mother(Joe, Mary) Mother (Sue, Mary) Age (Joe, 8) Age (Sue, 6) The following domain theory is helpful for acquiring the HouseMates concept: HouseMates(x, y) t InSameFamily(x, y) HouseMates(x, y) t FraternityBrothers (x, y) InSameFamily(x, y) t Married(x, y) InSame Family ( x y) t Youngster (x) A Youngster ( y ) A SameMother ( x, y ) و SameMother(x, y ) t Mother (x, z) A Mother (y, z ) Youngster (x) t Age(x, a ) A LessThan(a, 10) Apply the PROLOG-EBGalgorithm to the task of generalizing from the above instance, using the above domain theory. In particular, (a) Show a hand-trace of the…arrow_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_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
- Do you see yourself using email in the not-too-distant future? The path of an email message starts with the sender and concludes with the receiver of the message. Take careful notes on everything you discover. Is there a rationale to the differences, and if so, what are they? Consider the possibility that there exist several models, each of which has a unique level of complexity (or abstraction).arrow_forwardComputer Science You are told that state machine A has one input x, and one output y, both with type {1, 2}, and that it has states {a, b, c, d}. You are told nothing further. Do you have enough information to construct a state machine B that simulates A? If so, give such a state machine, and the simulation relation.arrow_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_forward
- 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_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
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