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- Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrač, Sašo Džeroski
- Challenges for Inductive Logic Programming
- Inductive logic programming - techniques and applications

## Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrač, Sašo Džeroski

Inductive logic programming ILP is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples. Inductive logic programming is particularly useful in bioinformatics and natural language processing.

Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. The term Inductive Logic Programming was first introduced [5] in a paper by Stephen Muggleton in The term " inductive " here refers to philosophical i.

The background knowledge is given as a logic theory B , commonly in the form of Horn clauses used in logic programming. A correct hypothesis h is a logic proposition satisfying the following requirements.

The following well-known example about learning definitions of family relations uses the abbreviations. Plotkin's [11] [12] " relative least general generalization rlgg " approach to inductive logic programming shall be used to obtain a suggestion about how to formally define the daughter relation dau.

The resulting Horn clause is the hypothesis h obtained by the rlgg approach. Concerning the above requirements, " Necessity " was satisfied because the predicate dau doesn't appear in the background knowledge, which hence cannot imply any property containing this predicate, such as the positive examples are. The common definition of the grandmother relation, viz. To overcome this flaw, that step has to be modified such that it can be parametrized with different literal post-selection heuristics.

First a hypothesis is searched with an inductive logic programming procedure, then a subset of the found hypotheses in most systems one hypothesis is chosen by a selection algorithm. A selection algorithm scores each of the found hypotheses and returns the ones with the highest score. An example of score function include minimal compression length where a hypothesis with a lowest Kolmogorov complexity has the highest score and is returned.

Therefore, an alternative hypothesis search can be conducted using the operation of the inverse subsumption anti-subsumption instead which is less non-deterministic than anti-entailment. Questions of completeness of a hypothesis search procedure of specific ILP system arise. For example, Progol's hypothesis search procedure based on the inverse entailment inference rule is not complete by Yamamoto's example. From Wikipedia, the free encyclopedia. Reprinted in J. Lassez, G. Plotkin Eds.

Algorithmic program debugging. Morgan Kaufmann Publishers Inc. A Perspective on Inductive Logic Programming. CiteSeerX : New Generation Computing. Artificial Intelligence. Meltzer, B. Machine Intelligence. Hybrid abductive inductive learning. Proceedings of the 13th international conference on inductive logic programming pp.

Berlin: Springer. Induction on failure: learning connected Horn theories. Proceedings of the 10th international conference on logic programing and nonmonotonic reasoning pp. Inverse subsumption for complete explanatory induction. Machine learning, 86 1 —, Which hypotheses can be found with inverse entailment?

In Inductive Logic Programming, pages — Springer, Learning definite and normal logic programs by induction on failure. PhD thesis, Imperial College London, Imparo is complete by inverse subsumption. BMC Bioinformatics. Muggleton, S. The Journal of Logic Programming. Lavrac, N. Inductive Logic Programming: Techniques and Applications.

New York: Ellis Horwood. Archived from the original on Retrieved Visual example of inducing the grandparenthood relation by the Atom system. Categories : Inductive logic programming. Hidden categories: CS1: long volume value. Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version.

## Challenges for Inductive Logic Programming

Inductive logic programming ILP is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. The term Inductive Logic Programming was first introduced [5] in a paper by Stephen Muggleton in The term " inductive " here refers to philosophical i.

## Inductive logic programming - techniques and applications

Publications: Inductive Logic Programming Inductive logic programming ILP studies the learning of Prolog logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional, feature-based representations of examples and concepts and cannot learn complex relational and recursive knowledge. ILP allows learning with much richer representations. Our work has focussed on applications of ILP to various problems in natural language and theory refinement for logic programs.

Inductive logic programming ILP is a research area that has its roots in inductive machine learning and logic programming. Computational logic has significantly influenced machine learning through the field of inductive logic programming ILP which is concerned with the induction of logic programs from examples and background knowledge. Machine learning, and ILP in particular, has the potential to influence computational logic by providing an application area full of industrially significant problems, thus providing a challenge for other techniques in computational logic.

Download to read the full article text. Aha, D. Learning singly recursive relations tronl small datasets.

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This book is an introduction to inductive logic programming ILP , a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs. The book extensively covers empirical inductive logic programming, one of the two major subfields of ILP, which has already shown its application potential in the following areas: knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases. The book provides the reader with an in-depth understanding of empirical ILP techniques and applications. It is divided into four parts. Part I is an introduction to the field of ILP.

Dai Wangzhou, Zhou Zhihua. Abstract: Inductive logic programming ILP is a subfield of symbolic rule learning that is formalized by first-order logic and rooted in first-order logical induction theories. The model learned by ILP is a set of highly interpretable first-order rules rather than black boxes; owing to the strong expressive power of first-order logic language, it is relatively easier to exploit domain knowledge during learning; the learned model by ILP can be used for modeling relationships between subjects, rather than predicting the labels of independent objects.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Predicate ranking algorithms and their application in an inductive logic programming system Abstract: Inductive logic programming ILP is a form of machine learning that induces rules from data using the language and syntax of logic programming.

*This book is an introduction to inductive logic programming ILP , a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs. The book extensively covers empirical inductive logic programming, one of the two major subfields of ILP, which has already shown its application potential in the following areas: knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases.*

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Fabrice F.This book is an introduction to inductive logic programming (ILP), a research field ; Hardcover/Paperback pages; eBook PDF files; Language: English with an in-depth understanding of empirical ILP techniques and applications.