2 edition of **analysis of refinement operators in inductive logic programming** found in the catalog.

analysis of refinement operators in inductive logic programming

Patrick van der Laag

- 323 Want to read
- 19 Currently reading

Published
**1995**
by Thesis Publishers in [Amsterdam
.

Written in English

- Logic programming.

This thesis develops a classification theory for search operators employed in incremental learning systems. It characterizes specialization and generalization operators in terms of their effectiveness and efficiency, and investigates the problems of combining these desirable properties. Applying this theory to the relational, first-order representations of Inductive Logic Programming (ILP), it provides positive and negative results concerning effective and efficient learning in practical ILP-systems.

**Edition Notes**

Statement | Patrick van der Laag. |

Series | Tinbergen Institute research series -- no. 102. |

The Physical Object | |
---|---|

Pagination | 160 p. : |

Number of Pages | 160 |

ID Numbers | |

Open Library | OL18087573M |

ISBN 10 | 9051703686 |

–Most important framework for inductive logic programming. Used by all major ILP systems. –S and G are single clauses –Combines propositional subsumption and subsumption on logical atoms –c1 theta-subsumes c2 if and only if there is a substitution θ such that c1 θ ⊆ c2 –c1: father(X,Y): parent(X,Y),male(X). Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down.

Tertius deals with learning first-order logic rules from the data that lack an explicit classification predicate. Learned rules are not restricted to predicate definitions as in supervised inductive logic programming. Tertius first performs an optimal search that tries to find the . Inductive Logic Programming L. De Raedt, K. Kersting. “Probabilistic inductive Logic Programming”. In S. Ben-David, J. Case and A. book author publisher publisher [illustration inspired by Lise Getoor]Real World book book book – Using refinement operators.

In order to leverage techniques from Inductive Logic Programming for the learning in description logics (DLs), which are the foundation of ontology languages in the Semantic Web, it is important to acquire a thorough understanding of the theoretical potential and limitations of using refinement operators within the description logic paradigm. In this paper, we present a comprehensive study. Start studying Inductive Logic Programming. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Search. refinement operator. operator that .

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Get this from a library. An analysis of refinement operators in inductive logic programming. [Patrick van der Laag]. from book Inductive Logic Programming, Inspired by inductive logic programming, refinement operators are used to construct a concept that generalizes positive examples while not encompassing.

Refinement Operators and Ideal Properties One of the major tasks in Inductive Logic Programming (ILP) is model inference (or concept learning), the induction of logical theories from examples. For a survey of the theory and methods of ILP, we Cited by: P.R.J.

van der Laag and S.H. Nienhuys-Cheng. A note on ideal refinement operators in ILP. In S. Wrobel, editor, Proceedings of the 4th International Workshop on Inductive Logic Programming, volume of GMD-Studien, pages – Gesellschaft für Mathematik und Datenverarbeitung MBH, September Google ScholarCited by: 5.

Home Browse by Title Books Inductive Logic Programming: 16th International Conference, ILPSantiago de Compostela, Spain, August, Revised Selected Papers An ILP Refinement Operator for Biological Grammar Learning.

from book Inductive Logic Programming: 18th International Conference, ILP Prague, Czech Republic, SeptemberProceedings (pp) A Note on Refinement Operators.

At the Interface of Inductive Logic Programming and Statistics. Pages Macro-Operators Revisited in Inductive Logic Programming. Inductive Logic Programming Book Subtitle 14th International Conference, ILPPorto, Portugal, September, Proceedings Editors.

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILPheld in Dubrovnik, Croatia, in September The 18 revi. Applying Inductive Logic Programming to Process Mining.

Pages Lamma, Evelina (et al.) Preview Buy Chap95 € A Refinement Operator Based Learning Algorithm for the $\mathcal{ALC}$ Description Logic. Pages Lehmann, Jens (et al.) Book Title Inductive Logic Programming Book Subtitle 17th International Conference, ILP. In Inductive Logic Programming (ILP), algorithms that are purely of the bottom-up or top-down type encounter several problems in practice.

Since a majority of them are greedy ones, these algorithms stop when finding clauses in local optima, according to the “quality” measure used for evaluating the results. stepwise (incremental) refinement of programs [24], which turn out to be too weak or too strong. The ideality of the refinement operators plays a key role when the efficiency and the effectiveness of the design process is an unnegligible requirement [46].

Theoretical studies in Inductive Logic Programming (ILP) have shown that, when a full Horn. Inductive Logic Programming Introduction to ILP Inductive Logic Programming (ILP) is a research area formed at the intersection of Machine Learning and Logic Programming. ILP systems develop predicate descriptions from examples and background knowledge.

The examples, background knowledge and final descriptions are all described as logic programs. Inductive 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. Macro-Operators Revisited in Inductive Logic Programming Bottom-Up ILP Using Large Refinement Steps On the Effect of Caching in Recursive Theory Learning FOIL-D: Efficiently Scaling FOIL for Multi-relational Data Mining of Large Datasets Learning an Approximation to Inductive Logic Programming Clause Evaluation.

scription logics compared to traditional logic programming based methods. In Section 9, we discuss related work, in particular the relation to re nement operators in the area of traditional Inductive Logic Programming.

Finally, in Section 10 we summarise our work and draw conclusions. Note. De clause grammars for language analysis { a survey of the formalism and a comparison with augmented transition networks.

Experiments in inductive chart parsing. Grammar learning using inductive logic programming. Inductive Logic Programming: Techniques and. The book contains a collection of eight survey papers written by some of the most excellent researchers in foundations of knowledge representation and reasoning.

It covers topics like theories of uncertainty, nonmonotonic and casual reasoning, logic and programming, abduction, inductive logic programming, description logics, complexity in. Foundations of Inductive Logic Programming.

SpringerVerlag,ISBN Nada Lavrač, and Sašo Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, Proceedings of the Conference on Inductive Logic Programming (ILP), since Audio Books & Poetry Community Audio Computers, Technology and Science Music, Arts & Culture News & Public Affairs Non-English Audio Spirituality & Religion.

Librivox Free Audiobook. Red Bear Studios SaskEV Leatherneck 11 MrMan01 Radio Monday Matinee Noterat podcast Mr. 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.

the success of Inductive Logic Programming (ILP), we pursue the transfer of ILP methods [13] to DLs, and in this paper we report on a resulting learning algorithm. Our approach is based on a thorough theoretical analysis of the po-tential and limitations of reﬁnement operators for DLs.

We make the following contributions.Applying Inductive Logic Programming to Process Mining.- A Refinement Operator Based Learning Algorithm for the Description Logic.- Foundations of Refinement Operators for Description Logics.- A Relational Hierarchical Model for Decision-Theoretic Assistance.- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming Interest in inductive logic programming has waxed and waned over the last decade, but never fallen to zero.

This book is a summary of what was known in the field inand much has changed since then. It can however still serve as an introduction to the field of inductive logic programming, in spite of its publication s: 1.