Inductive Logic ProgrammingInductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level. * * Examination of the background to current developments within the area * Identification of the various goals and aspirations for the increasing body of researchers in inductive logic programming * Coverage of induction of first order theories, the application of inductive logic programming and discussion of several logic learning programs * Discussion of the applications of inductive logic programming to qualitative modelling, planning and finite element mesh design |
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Page 165
It is based on the early systems MIS [ 34 ] and MARVIN ( 33 ) but it incorporates
some ideas of DISCIPLE ( 36 ) and BLIP ( 20 , 39 ) . The main design goal for
CLINT was to develop a user - friendly interactive concept learner . With this in
mind ...
It is based on the early systems MIS [ 34 ] and MARVIN ( 33 ) but it incorporates
some ideas of DISCIPLE ( 36 ) and BLIP ( 20 , 39 ) . The main design goal for
CLINT was to develop a user - friendly interactive concept learner . With this in
mind ...
Page 413
CLINT however first constructs a minimal generalization of an hypothesis clause
consistent with N , then generates an example that is covered by this
generalization but not by the old clause , and , if the example is positive , the old
clause is ...
CLINT however first constructs a minimal generalization of an hypothesis clause
consistent with N , then generates an example that is covered by this
generalization but not by the old clause , and , if the example is positive , the old
clause is ...
Page 414
in fact completely saturated clauses ( constructed by CLINT from an example , in
a certain hypothesis language ) . On these starting clauses generalization
operators are applied , as already mentioned in the previous Section . For the
moment ...
in fact completely saturated clauses ( constructed by CLINT from an example , in
a certain hypothesis language ) . On these starting clauses generalization
operators are applied , as already mentioned in the previous Section . For the
moment ...
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Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
oor A | 22 |
Copyright | |
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Common terms and phrases
algorithm allows applied approach arguments assume background knowledge base body called CIGOL CLINT complete Computer concept consistent constrained atoms constraint constructed contains correct corresponding covers defined definition derivation described domain theory efficient equivalent examples exists explanation expression extend facts false Figure finite first-order formula function given GOLEM ground head Horn clauses hypothesis implied inductive inference input instances Intelligence introduced inverse knowledge knowledge base language least limit literals Logic Programming Machine Learning method Muggleton negative examples non-monotonic Note occur operator ordinary polynomial positive positive examples possible predicates present problem Proceedings proof properties prove queries reasoning relation replacing representation representative resolution respect restricted result rules saturation sentences similar sorted atoms space specialization specific step structure substitution symbol Theorem theory tree true values variables