Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Textbook Question
Chapter 2, Problem 2E
Let us examine the rationality of various vacuum-cleaner agent functions.
- a. Show that the simple vacuum-cleaner agent function described in Figure 2.3 is indeed rational under the assumptions listed on page 38.
- b. Describe a rational agent function for the case in which each movement costs one point. Does the corresponding agent program require internal state?
- c. Discuss possible agent designs for the cases in which clean squares can become dirty and the geography of the environment is unknown. Does it make sense for the agent to learn from its experience in these cases? If so, what should it learn? If not, why not?
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Students have asked these similar questions
For 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.
A. What has to be done if there is any change in the environment properties for a simple
reflex agent?
Answer:
B. Name one advantage and one disadvantage of bidirectional heuristic search? Also, when
can't we use the bidirectional search?
Answer:
C. Is it possible for an unknown environment to be fully observable? Justify your answer.
Answer:
Explain in your own words the difference between an ideal rational agent and an omniscient agent.?
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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- the knowledge-based agent is not an arbitrary program for calculating actions. It is amenable to a description at the knowledge level, where we need specify only what the agent knows and what it goains are, in order to fix its behavior. Give an Example:arrow_forwardConsider the following set of training examples: (a) What is the entropy of this collection of training examples with respect to the target function classification? (b) What is the information gain of az relative.to these training examples?arrow_forward2. Model a state machine for a chess game using the following description: A chess game consists of alternate moves of Black and White. White moves first (start of the game). The game can end both when it is White's and when it is Black's turn. The moving player can end the game: winning (checkmate), loosing (resign), or with a draw.arrow_forward
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