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 177
more than one literal in a violated constraint , the responsible examples have to
be identified by performing credit ... As an example of credit - assignment ,
consider the following constraint : is allowed _ to _ drive ( X , Y ) , isa ( Y , car ) +
license ...
more than one literal in a violated constraint , the responsible examples have to
be identified by performing credit ... As an example of credit - assignment ,
consider the following constraint : is allowed _ to _ drive ( X , Y ) , isa ( Y , car ) +
license ...
Page 384
eligible for deferment is true of two positive examples and no negative examples ,
and has the maximum information gain . The clause disability deferment is true of
the two positive examples and no negative examples , and is operationalized ...
eligible for deferment is true of two positive examples and no negative examples ,
and has the maximum information gain . The clause disability deferment is true of
the two positive examples and no negative examples , and is operationalized ...
Page 483
1 100 % W NAWN of positive examples ( all rules performed 100 % correct on the
negative examples ) . Note that the only difference made by changing the depth
was a slight increase in the CPU time spent to complete induction . Also , in all ...
1 100 % W NAWN of positive examples ( all rules performed 100 % correct on the
negative examples ) . Note that the only difference made by changing the depth
was a slight increase in the CPU time spent to complete induction . Also , in all ...
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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
References to this book
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 |
| Export Citation | BiBTeX EndNote RefMan |