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 47
Notice that the head of the LGG ! , is the least ordinary generalization of the
atoms in E . Notice also that the constraint of the LGGs , is the strongest possible
constraint : strengthening the constraint yields a constrained atom that is not E -
more ...
Notice that the head of the LGG ! , is the least ordinary generalization of the
atoms in E . Notice also that the constraint of the LGGs , is the strongest possible
constraint : strengthening the constraint yields a constrained atom that is not E -
more ...
Page 59
O Notice that the size of a maximum chain does not increase if we allow the head
, B , of the bottom constrained atom , ß , to have head size greater than n but still
require all others to have head size at most n . To see this , note that if B has ...
O Notice that the size of a maximum chain does not increase if we allow the head
, B , of the bottom constrained atom , ß , to have head size greater than n but still
require all others to have head size at most n . To see this , note that if B has ...
Page 282
The following definitions are provided as background knowledge : partition ( _ X ,
1 ) , ( 1 , 1 ) ) . partition ( X , ( Head | Tail ) , ( Head | Sublisti ) , Sublist2 ) - Ite (
Head , X ) , partition ( X , Tail , Sublist1 , Sublist2 ) . partition ( X , ( Head | Tail ] ...
The following definitions are provided as background knowledge : partition ( _ X ,
1 ) , ( 1 , 1 ) ) . partition ( X , ( Head | Tail ) , ( Head | Sublisti ) , Sublist2 ) - Ite (
Head , X ) , partition ( X , Tail , Sublist1 , Sublist2 ) . partition ( X , ( Head | Tail ] ...
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Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
oor A | 22 |
Copyright | |
26 other sections not shown
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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
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Bibliographic information
| Title | Inductive Logic Programming Issue 38 of A.P.I.C. studies in data processing, ISSN 0067-2483 Issue 38 of APIC Series, Automatic Programming Information Centre Brighton Volume 38 of Automatic Programming Information Centre Brighton: The APIC series |
| Editor | Stephen Muggleton |
| Edition | illustrated |
| Publisher | Academic Press, 1992 |
| Original from | the University of Michigan |
| Digitized | 16 Nov 2007 |
| ISBN | 0125097158, 9780125097154 |
| Length | 565 pages |
| Subjects | › › Computers / Artificial Intelligence / General Computers / Programming / Object Oriented |
| Export Citation | BiBTeX EndNote RefMan |