Artificial Intelligence: A Modern Approach
3rd Edition
ISBN: 9780136042594
Author: Stuart Russell, Peter Norvig
Publisher: Prentice Hall
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Chapter 6, Problem 4E
Explanation of Solution
a.
Formulation:
- For linear floor planning, the one possibility is to have a variable for each of the small rectangles with the value of each variable being a 4-tuple consisting of the “x” and “y” coordinated of the upper left and lower right corners of the place where the rectangle will be located.
- The domain of each variable is the set of 4-tuples...
Explanation of Solution
b.
Formulation:
- There is one possibility in class scheduling is to have three variables for each class.
- One with times for values. For example, MWF8:00, TuTh8:00, MWF9:00.
- Another possibility is one with classrooms for values. For example, Wheeler110, Evans330 and so on.
- Another possibility is one with instruction for values...
Explanation of Solution
c.
Formulation:
- One possibility for Hamiltonian tour is to have one variable for each stop on the tour...
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Chapter 6 Solutions
Artificial Intelligence: A Modern Approach
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Similar questions
- 1. Let S = {1,2...n} be a set activities, where activity i € S has a start time si, a finish time f; and a profit p, asSociated with it. Design an dynamic programming algorithm for computing a set of compatible activities of maximum profit. Argue that the time complexity of you algorithm is O(n log(n)). (You must start with the correct definitions, and then write a recurrence relation.)arrow_forward8. A school is creating class schedules for its students. The students submit their requested courses and then a program will be designed to find the optimal schedule for all students. The school has determined that finding the absolute best schedule cannot be solved in a reasonable time. Instead they have decided to use a simpler algorithm that produces a good but non-optimal schedule in a more reasonable amount of time. Which principle does this decision best demonstrate? A. Unreasonable algorithms may sometimes also be undecidable B. Heuristics can be used to solve some problems for which no reasonable algorithm exists C. Two algorithms that solve the same problem must also have the same efficiency D. Approximate solutions are often identical to optimal solutions 0000arrow_forwardSimple example in healthcare: Let consider 7 patients with different surgeries times: ST=[4h30, 4h, 3h, 3h, 2h30, 1h30, 1h] We have to decide how to assign patients to the operating rooms. First problem: let consider 3 operating rooms available with their corresponding capacities OR=[7h30, 4h, 4h]. We want to operate the maximum number of patients. Propose a model and solve it using CPLEXarrow_forward
- QUESTION THREE A. Consider the thirsty person problem given below: To drink, a thirsty person must have three things; water, ice and a glass. There are three thirsty people, each having a different one (and only one) of the three required items. A fourth person, a server has unlimited supply of all three items. If nobody is drinking, the server places two of the three items (chosen at random) onto table. Thirsty person who can make a drink from those two items will pick them up and drink a glass of ice water. When done, thirsty person will notify the server and the process will repeat. Write a process that will control the thirsty person and the server using semaphores. (B) (i)What is a critical section in code? (ii) Explain the three properties that any solution to the Critical Section Problem should guarantee. (C) Explain the role the Operating System plays in Garbage-In-Garbage-Out (GIGO).arrow_forwardknapsack problem: given the first table: c beeing value and w beeing weight, W max weight. I got table 2 as a solution to: 2 Solve the Knapsack problem with dynamic programming. To do this, enter the numbers Opt[k,V ] for k = 1,...,5 and V = 1,...,9 in a table. Here Opt[k, V ] is the partial solution obtained for the first k items with maximum weight V. " Can somebody explain me the values of the table? How do they get calculated? Also how do i solve the followup-task: Using the values in the table, determine a solution OPTSOL(I)=(β1,β2,β3,β4), starting with β4. (Use backtracing to do this)arrow_forwardIt has been observed by management that some faculty members at theuniversity demonstrate a lackadaisical attitude toward teaching. They seldom go to class yet at the end of each month they receive full salary. Management has decided that GHs 200, 300, 400, and 500 will be deducted from a faculty’s salary if he/she offends once, twice, third time and forth time respectively in a month. This means that if a faculty member offends once in a month GHs200 will be deducted, if a faculty member offends twice in a month, GHs 500 (i.e. 200+300) will be deducted, if a faculty member offends three times in a month GHs 900 (i.e. 200+300+400), will be deducted and if a faculty member offends four times in a month GHs 1,400 (i.e. 200+300+400+500). Assuming all faculty members are on a flat salary rate of GHs 2000.a. Write a program to request for the names of four faculty members and number times he/she has absented him/herself from class in a month.One of them should have absented him/herself…arrow_forward
- Consider the following instance of 0-1 Knapsack problem: Item 11 12 14 15 Profit 6 5 9 Weight 5 5 8 The maximum weight of 16 Kg is allowed in the knapsack. The assumption is that each time an item is either taken completely or is left behind; she cannot take a fractional amount of an item or take an item more than once. Given the above set-up, using BRANCH AND BOUND methodology pick a subset of these items such that their total weight is no more than 16 Kg and their total value is maximized.arrow_forward8. A school is creating class schedules for its students. The students submit their requested courses and then a program will be designed to find the optimal schedule for all students. The school has determined that finding the absolute best schedule cannot be solved in a reasonablo time. Instead they have decided to use a simpler algorithm that produces a good but non-optimal schedule in a more reasonable amount of time. Which principle does this decision best demonstrate? O A Unreasonable algorithms may sometimes also be undecidable O B. Heuristics can be used to solve some problems for which no reasonable algorithm exsts O C. efficiency Two algorithms that solve the same problem must also have the same O B. Approximate solutions are often identical to optimal solutionsarrow_forwardBuiding an optimization model for the building a house problem, and solve it using JuMP and Clp.Example: building a house A small sample: Let t1,t0,tm,tn,tt,ts be start times of the associated tasks. Now use the graph to write the dependency constraints: Tasks 0,m, and n can't start until task I is finished, and task I takes 3 days to finish. So the constraints are: t1+3≤3≤t0,t1+3≤tm,t1+3≤tn Task t can't start until tasks m and n are finished. Therefore: tm+1≤tt,tn+2≤tt,takes 3 days to finish. So the constraints are: Task s can't start until tasks 0 and t are finished. Therefore: t0+3≤ts,tt+3≤tsarrow_forward
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