9 edition of **Probabilistic inductive logic programming** found in the catalog.

- 321 Want to read
- 35 Currently reading

Published
**2008**
by Springer in Berlin, New York
.

Written in English

- Logic programming,
- Machine learning,
- Stochastic processes

**Edition Notes**

Statement | Luc De Raedt ... [et al.] (eds.). |

Series | LNCS sublibrary, Lecture notes in computer science -- 4911. -- Lecture notes in artificial intelligence, State-of-the-art survey, Lecture notes in computer science -- 4911., Lecture notes in computer science, Lecture notes in computer science |

Contributions | Raedt, Luc de, 1964- |

Classifications | |
---|---|

LC Classifications | QA76.63 .P69 2008 |

The Physical Object | |

Pagination | viii, 339 p. : |

Number of Pages | 339 |

ID Numbers | |

Open Library | OL16917873M |

ISBN 10 | 3540786511 |

ISBN 10 | 9783540786511 |

LC Control Number | 2008922315 |

The book gives a nice introduction to inductive logic.’ Harry Gensler Source: The Times Higher Education Supplement 'This is, as intended, a very introductory text in probability and inductive logic.' Source: Zentralblatt für Mathematik. The book has been designed to offer maximal accessibility to the widest range of students (not only those majoring in philosophy) and assumes no formal training in elementary symbolic logic. It offers a comprehensive course covering all basic definitions of induction and probability, and considers such topics as decision theory, Bayesianism Price: $

Introduction Probabilistic Abductive Logic Programming Experimental Evaluation Conclusions References Improving Classiﬁcation Exploiting Probabilistic Abductive Reasoning Exploiting our probabilistic abductive logic proof procedure learns the model (i.e. the Abductive Logic Program) and the parameters (i.e. literals probabilities. (inductive) logic programming and probabilistic programming languages (Roy et al. ) has resulted in a wide variety of different formalisms, models and languages, with applica- tions in structured, uncertain domains such as natural language processing, bioinformatics.

An Abductive-Inductive Algorithm for Probabilistic Inductive Logic Programming Stanislav Dragiev, Alessandra Russo, Krysia Broda, Mark Law, and Rares Turliuc Department of Computing, Imperial College London, United Kingdom {sd, , kb, ml, c}@ Abstract. The integration of abduction and induction has. Dynamically ordered probabilistic choice logic programming. In Proc. of the 20th Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS). Volume of LNCS, pages – Springer Verlag, London, UK.

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This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory. Probabilistic inductive logic programming aka.

statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic. Probabilistic Inductive Logic Programming Theory and Applications. book series (LNCS, volume ) Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI (\cal{BN}\)): Constraint Logic Programming for Probabilistic Knowledge.

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with. Probabilistic Inductive Logic Programming. Pages *immediately available upon purchase as print book shipments may be delayed due to the COVID crisis.

ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included. 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.

probability theory, logic programming and machine learning [39,15,41,30,34, 17,24,20,2,23]. This work is known under the names of statistical relational learning [14,11], probabilistic logic learning [9], or probabilistic inductive logic programming.

Whereas most of. This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILPheld in Orléans, France, in September The 12 full papers presented were carefully reviewed and selected from numerous submissions.

Keywords: logic programming, probabilistic programming, inductive logic programming, probabilistic logic programming, statistical relational learning 1. INTRODUCTION Probabilistic Logic Programming (PLP) started in the early 90s with seminal works such as those of Dantsin (), Ng and Subrahmanian (), Poole (), and Sato ().

Logic Learning Probabilistic Inductive Logic Programming *. De Raedt, K. Kersting. ÒProbabilistic inductive Logic Programming Ó. In S. Ben-David, J. Case and A. Maruoka, editors, Proceedings of the 15th International Conference on Algorithmic Learning Theory (ALT), pages Padova, Italy, October.

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Probabilistic Inductive Logic Programming (Lecture Notes in Computer Science ()) [De Raedt, Luc, Frasconi, Paolo, Kersting, Kristian, Muggleton, Stephen H.] on *FREE* shipping on qualifying offers. Probabilistic Inductive Logic Programming (Lecture Notes in 5/5(1). The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov.

The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal result is a richer and more expressive formalism with a broad range of possible application areas.

Probabilistic logics attempt to find a natural extension of. Within this book, the author makes several major contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values.

The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP).

Books. The Master Algorithm:K. Kersting and S. Muggleton (eds.), Probabilistic Inductive Logic Programming (pp. ), New York: Springer. Markov Logic: A Unifying Framework for Statistical Relational Learning, with Matt Richardson.

In L. Getoor and B. Taskar (eds.), Introduction to Statistical Relational Learning (pp. An Abductive-Inductive Algorithm for Probabilistic Inductive Logic Programming Stanislav Dragiev, Alessandra Russo, Krysia Broda, Mark Law, and Rares Turliuc Department of Computing, Imperial College London, United Kingdom fsd,kb, ml, [email protected] Abstract.

The integration of abduction and induction has. An Introduction to Probability and Inductive Logic is a very good book. The explanations are clear, the examples are lively and the book does a good job of surveying the major points of this very important topic.

I wish that other reviewers had stated more clearly that the intended audience is s: Probabilistic inductive logic programming aka.

statistical relational learning addresses one of the central questions of artificial intelligence: the inte- gration of probabilistic reasoning with. Logic programming, abduction and probability – a top-down anytime algorithm for estimating prior and posterior probabilities.

New Generation Computing, 11(3)–, [20] Luc De Raedt and Kristian Kersting. Probabilistic inductive logic programming. In ALTvolume of LNCS, pages 19– Springer, The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision.

[Paper, Code] Hai Wang and Hoifung Poon. In Proceedings of the Annual Conference of Empirical Methods in Natural Language Processing (EMNLP), November EZLearn: Exploiting Organic Supervision in Automated Data Annotation.

Maxim Grechkin, Hoifung Poon, Bill Howe.