Consider learning the target concept Good CreditRisk defined over instances de- scribed by the four attributes HasStudentLoan, HasSavings Account, Isstudent, OwnsCar. Give the initial network created by KBANN for the following domain theory, including all network connections and weights. GoodCreditRiskt Employed, LowDebt Employed t-1sStudent LowDebt t-HasStudentLoan, HasSavingsAccount 12.2. KBANN converts a set of propositional Horn clauses into an initial neural network. Consider the class of n-of-m clauses, which are Horn clauses containing m literals in the preconditions (antecedents), and an associated parameter n where n m. The preconditions of an n-of-m Horn clause are considered to be satisfied if at least n of its m preconditions are satisfied. For example, the clause Student t LivesInDorm, Young, Studies; n = 2 asserts that one is a Student if at least two of these three preconditions are satisfied. Give an algorithm similar to that used by KBANN, that accepts a set of propositional n-of-m clauses and constructs a neural network consistent with the domain theory.
Consider learning the target concept Good CreditRisk defined over instances de- scribed by the four attributes HasStudentLoan, HasSavings Account, Isstudent, OwnsCar. Give the initial network created by KBANN for the following domain theory, including all network connections and weights. GoodCreditRiskt Employed, LowDebt Employed t-1sStudent LowDebt t-HasStudentLoan, HasSavingsAccount 12.2. KBANN converts a set of propositional Horn clauses into an initial neural network. Consider the class of n-of-m clauses, which are Horn clauses containing m literals in the preconditions (antecedents), and an associated parameter n where n m. The preconditions of an n-of-m Horn clause are considered to be satisfied if at least n of its m preconditions are satisfied. For example, the clause Student t LivesInDorm, Young, Studies; n = 2 asserts that one is a Student if at least two of these three preconditions are satisfied. Give an algorithm similar to that used by KBANN, that accepts a set of propositional n-of-m clauses and constructs a neural network consistent with the domain theory.
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Transcribed Image Text:Consider learning the target concept GoodCredit Risk defined over instances de-
scribed by the four attributes HasStudentLoan, HasSavings Account, Isstudent,
OwnsCar. Give the initial network created by KBANN for the following domain
theory, including all network connections and weights.
GoodCreditRiskt Employed, LowDebt
Employed t -1sStudent
LowDebt t -HasStudent Loan, HasSavings Account
12.2. KBANN converts a set of propositional Horn clauses into an initial neural network.
Consider the class of n-of-m clauses, which are Horn clauses containing m literals
in the preconditions (antecedents), and an associated parameter n where n m.
The preconditions of an n-of-m Horn clause are considered to be satisfied if at least
n of its m preconditions are satisfied. For example, the clause
Student t LivesInDorm, Young, Studies; n = 2
asserts that one is a Student if at least two of these three preconditions are satisfied.
Give an algorithm similar to that used by KBANN, that accepts a set of
propositional n-of-m clauses and constructs a neural network consistent with the
domain theory.
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