Inductive Logic Programming: From Machine Learning to Software Engineering (Logic Programming) by Francesco Bergadano

Inductive Logic Programming: From Machine Learning to Software Engineering (Logic Programming) by Francesco Bergadano

Author:Francesco Bergadano
Language: eng
Format: epub
Published: 2011-08-22T23:40:00+00:00


instantiates to the following two clauses:

append(X,Y,Z) :- cons(X1,X2,X), no_op(X1,Y). append(X2,Y,W), cons(X1,W,Z).

where we may think of a given background knowledge including:

cons(X.Y.[XIY]).

Inenlber(X. [XIY] ).

n1eci1ber(X.[_ Y]) :- inc~iiilx'r(X,Y).

no-op(X,Y).

As we have seen. a rule model may be instantiated to clauses serving quite different purposes, depending on which actual predicates are used. In R.DT, the choice of the predicates is also constrained by a so-called topology, which is a kind of dependency graph appropriate for the application domain: the predicate in the consequent, of a clause is required to depend on the predicates in the antecedent. The details of this mechanism for limiting the possible instantiations of predicate variables are found in [107]. A similar notion of dependency is found in S. Russell's determinations [170].

The notion of rule model has recently been extended to more general clause templates by Tausend [1901. A main improvement over rule models consists in the fact that the arguments in a literal may also be abstracted into a variable. This will allow us to provide schemata for defining predicates of different arity. For instance, the following clause:



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