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 132
In this paper , we take a broad view of inverse resolution in the learning process ,
and bring out a critical comparison and draw some analogy between each of
these learning methods . It turns out that most of the work done in this area is a ...
In this paper , we take a broad view of inverse resolution in the learning process ,
and bring out a critical comparison and draw some analogy between each of
these learning methods . It turns out that most of the work done in this area is a ...
Page 410
1 Completeness of Inverse Resolution In this Section we discuss some
completeness results for inverse resolution . The discussion reveals that the
search space can become very large . Therefore we argue that inverse resolution
should be ...
1 Completeness of Inverse Resolution In this Section we discuss some
completeness results for inverse resolution . The discussion reveals that the
search space can become very large . Therefore we argue that inverse resolution
should be ...
Page 415
To make inverse resolution a useful and interesting operator in a concept
learning algorithm , one needs to formulate a number of heuristics in order to
restrict the search space . However , such restrictions can harm the completeness
of the ...
To make inverse resolution a useful and interesting operator in a concept
learning algorithm , one needs to formulate a number of heuristics in order to
restrict the search space . However , such restrictions can harm the completeness
of the ...
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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
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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 / Intelligence (AI) & Semantics Computers / Programming / Object Oriented |
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