Mom, Dad, Baby, Student, Teacher, and Guide are lining up next to each other in six linear spots labeled 1 to 6, one to a spot. Baby needs to line up between Mom and Dad. Student and Teacher need to be next to each other. Guide needs to be at one end, in spot 1 or 6. Formulate this problem as a CSP: list the variables, their domains, and the constraints. Encode unary constraints as a constraint rather than pruning the domain.

Mom, Dad, Baby, Student, Teacher, and Guide are lining up next to each other in six linear spots labeled 1 to 6, one to a spot. Baby needs to line up between Mom and Dad. Student and Teacher need to be next to each other. Guide needs to be at one end, in spot 1 or 6. Formulate this problem as a CSP: list the variables, their domains, and the constraints. Encode unary constraints as a constraint rather than pruning the domain.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question 1
Consider a problem with four variables, {A,B,C,D}. Each variable has domain {1,2,3}. The constraints on the problem are that A > B, B < C, A = D, C ¹ D. Perform variable elimination to remove variable B. Explain the process and show your work.
1. A known relationship mode of R (U, F), U= (A, B, C, E, F, G, H, I, J, G); F={AB->E,ABE->FG, B->FI, C->J, CJ->I, G->H}; find the minimum cover of F, then use theNormal Form decomposition algorithm to decompose this mode to 3NF, and check if thedecomposition has the nonadditive join property.
1.A known relationship mode of R (U, F), U= (A, B, C, D, E, G); F={AB->C, D->EG,C->A, BE->C, BC->D, CG->BD, ACD->B, CE->AG}; find the minimum cover of F,then use the Normal Form decomposition algorithm to decompose this mode to 3NF, andcheck if the decomposition has the nonadditive join property.
1. A known relationship mode of R (U, F), U= (c, t, h, r, s, g, q); F={c->tq, cs->g, ht->r,hr->c, hs->r}; find the minimum cover of F, then use the Normal Form decompositionalgorithm to standardize this mode to 3NF, and check if the decomposition has thenonadditive join property.
Given the following UML diagram: Student - studentID: String + getStudentID(): String + toString(): String Person - lastName: String - firstName: String - dateOfBirth: Date - address: Address + getName(): String + getDOB(): Date + getAddress(): Address + toString(): String Professor - employeeID: String +getEmployelD(): String +toString(): String Write an implementation for the class Professor. DO NOT include or implement any constructor for this class. There are no abstract methods/classes in this example. toString method will return any simple String that contains firstName and employeelD only; no less, no more!! Assume firstName was defined as protected in class Person. You are not allowed to add implementation for any class or methods other than what is listed in the question statement.
You are to produce a report on Model-based RL and Model-free RL. Discuss about Model-based and Model-free RL. Compare the algorithms of Model-based vs. Model-free RL.
1)Suggest a situation where you would want a model with a very large vector size and a situation where a large vector would be unnecessary ? 2) Describe what vector size represents in the sparce implementation of BEAGLE and what environmental vectors are ?
Given: Relation R = {(a,a), (a.b), (b,a). (b,b). (c,c)} and Set S = {a, b, c} Complete Relation R to be Transitive with Set S
Can you please help me with part 2 of this question? Thanks.
Why don’t we allow an LP to have < or > constraints? Why <= or >= constraints?
Computer Science Text categorization is the task of assigning a given document to one of a fixed set of categories on the basis of the text it contains. Naive Bayes models are often used for this task. In these models, the query variable is the document category, and the “effect” variables are the presence or absence of each word in the language; the assumption is that words occur independently in documents, with frequencies determined by the document category. a. Explain precisely how such a model can be constructed, given as “training data” a set of documents that have been assigned to categories. b. Explain precisely how to categorize a new document. c. Is the conditional independence assumption reasonable? Discuss