Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 2, Problem 11E
a.
Explanation of Solution
Simple reflex agent being rational
- Unless the agent randomizes, it will stuck forever against a wall when it tries to move in a direction that is blocked...
b.
Explanation of Solution
Simple reflex agent with randomized agent function
- One possible design that cleans up dirt or otherwise moving randomly is
(defun randomized-reflex-vacuum-agent (percept)
(destructuring-bind (location status) percept
(...
c.
Explanation of Solution
Randomized agent performing poorly
- Students wish to measure clean-up time for linear or square environments...
d.
Explanation of Solution
Randomized agent with state outperforming simple reflex agent
- Rational behaviour in unknown environments is a complex one and is worth encouraging...
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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:
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:
Consider an agent for a vacuum cleaner environment in which the geography of the environment (extent, boundaries, and obstacles) is unknown as is the initial dirt configuration. The agent can go up and down as well as left and right. Can a simple reflex agent be perfectly rational for this environment? Explain in a few sentences using an example scenario
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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- A fictitious setting, JUNGLE, is being described in PDDL terminology. There are three predicates in this universe, and each one may have a maximum of four arguments. There should be a limit on the number of JUNGLE states. It's important to provide an explanation.arrow_forwardConsider 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_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
- 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.arrow_forwardFor the goal-based agent architecture given in the picture, write the pseudocode for the agent, given the following: function GOAL-BASED-AGENT (percept) returns an action persistent: state, the agent’s current conception of the world state model, a description of how the next state depends on the current state and action goal, a description of the desired goal state plan, a sequence of actions to take, initially empty action, the most recent action, initially nonearrow_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_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_forwardConsider 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_forward- Give an example of a reinforcement learning application that can be modeled by a POMDP. Define the states, actions, observations, and re- ward.arrow_forward
- Write 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_forwardFormulate your own argument (make it creative!) and draw a suitable Euler diagram for it. Justify as well whether it is valid or not. You may emulate the four given arguments below. Example: All Filipinos enjoy singing. Juan is a Filipino. Therefore, Juan enjoys singing. Some physicists are poets. Einstein is a physicist. Therefore, Einstein is a poet. All lions are animals. Some lions have manes.Therefore, some animals have manes. All booms (B) are zooms (Z). All feeps (F) are meeps (M). No boom is a feep. Therefore, no zoom is a meep.arrow_forwardThe PDDL is put to use in order to provide a description of a made-up setting known as the JUNGLE. This universe has a total of five constants and three predicates, each of which may take a maximum of four arguments. There should be a limit placed on the total number of states on this JUNGLE planet. Do we need to offer justification for this?arrow_forward
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