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 59
Substituting variables that already occur in a for other variables in a reduces
height - bound ( a ) because it reduces the number , m , of variables in a , and it
changes the values of no other parameters in the definition of heightbound ( a ) .
Substituting variables that already occur in a for other variables in a reduces
height - bound ( a ) because it reduces the number , m , of variables in a , and it
changes the values of no other parameters in the definition of heightbound ( a ) .
Page 324
Suppose we have a clause c with Old distinct variables ( referred to as old
variables ) . The number of literals added in each case is given below : 1 . bj = 2 x
( Old ) = Old ( Old – 1 ) : there are ( Old ) distinct choices of pairs of old variables ;
2 .
Suppose we have a clause c with Old distinct variables ( referred to as old
variables ) . The number of literals added in each case is given below : 1 . bj = 2 x
( Old ) = Old ( Old – 1 ) : there are ( Old ) distinct choices of pairs of old variables ;
2 .
Page 347
This is because rule models can be neither true nor false ; only instantiations of
rule schema where all predicate variables are replaced with predicates can be
true or false . These considerations lead directly to the definition of our generality
...
This is because rule models can be neither true nor false ; only instantiations of
rule schema where all predicate variables are replaced with predicates can be
true or false . These considerations lead directly to the definition of our generality
...
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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 / Artificial Intelligence / General Computers / Programming / Object Oriented |
| Export Citation | BiBTeX EndNote RefMan |