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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Students have asked these similar questions
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:
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:
Give 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 bananas
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
- 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 scenarioarrow_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_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
- 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_forwardIs it possible for a single operation that is in the midst of being carried out to get halted while it is still being carried out? It is expected that any remarks you give will be backed by rational justification in some way.arrow_forwardUse the predicates Color(x,y,t) – the color of x is y at time t, Later(x,y) – x is later than y, and the constant L1. Write a set of three sentences to describe the behavior of traffic light L1. Its color cycles through the sequence Green -> Yellow -> Red -> Green -> ...arrow_forward
- A robot vacuum cleaner uses a fuzzy logic system to control the speed of the suction of the dirt on the floor based on two inputs : Amount of dirt and Position of the vacuum in the room. Suggest the linguistic variables for the inputs and the output of the system and their corresponding linguistic values. a. b. Draw the membership functions for the linguistic variables. C. Create the related fuzzy rules for the system (up to you to create how many rules). d. Use your own examples for the amount of dirt and the position in the room to show how your system can give the speed of suction of the vacuum cleaner.arrow_forwarddescribes the FSM with a natural language, which is typical at the highlevel design phase of a software development process. Refine the FSM so that it iscloser to the implementation by incorporating a more formal handling of the sensoryevents sensor(l, f, r). Boolean variables l, f , and r indicate whether there is a wallon the neighbouring left, front, or right tile. Also introduce a simple local variableinterface for executing the actions ‘go forward’, ‘turn left 90◦’, and ‘turn right 90arrow_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_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
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