103 research outputs found
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 17382 "Approaches and Applications of Inductive Programming". After a short introduction to the state of the art to
inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given
05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis
No full textA short report on the Dagstuhl seminar on Probabilistic, Logical and Relational Learning – Towards a Synthesis is given
05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis
No full textFrom 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202)
No full textIn this report the program and the outcomes of Dagstuhl Seminar 19202 "Approaches and Applications of Inductive Programming" is documented. After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given
07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis
No full textFrom April 14 – 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learning - A Further Synthesis'' was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
The PITA System for Logical-Probabilistic Inference
No full textIntroduction,
Probabilistic Logic Programming,
The PITA System,
Experiments,
Bibliograph
Machine learning a probabilistic network of ecological interactions
No full textAbstract. In this paper we demonstrate that machine learning (using Abductive ILP) can generate plausible and testable food webs from eco-logical data. In this approach, unlike previous applications of Abductive ILP, the abductive predicate ‘eats ’ is entirely undefined before the start of the learning. We also explore a new approach, called Hypothesis Fre-quency Estimation (HFE), for estimating probabilities for hypothetical ‘eats ’ facts based on their frequency of occurrence when randomly sam-pling the hypothesis space. The results of cross-validation tests suggest that the trophic networks with probabilities have higher predictive accura-cies compared to the networks without probabilities. The proposed trophic networks have been examined by domain experts and comparison with the literature shows that many of the links are corroborated by the literature. In particular, links ascribed with high frequency are shown to correspond well with those having multiple references in the literature. In some cases novel high frequency links are suggested, which could be tested.
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 15442 "Approaches and Applications of Inductive Programming". After a short introduction to the state of the art to inductive programming research, an overview of the talks and the outcomes of discussion groups is given
Completing Inverse Entailment
No full text. Yamamoto has shown that the Inverse Entailment (IE) mechanism described previously by the author is complete for Plotkin's relative subsumption but incomplete for entailment. That is to say, an hypothesised clause H can be derived from an example E under a background theory B using IE if and only if H subsumes E relative to B in Plotkin's sense. Yamamoto gives examples of H for which B [ H j= E but H cannot be constructed using IE from B and E. The main result of the present paper is a theorem to show that by enlarging the bottom set used within IE, it is possible to make a revised version of IE complete with respect to entailment for Horn theories. Furthermore, it is shown for function-free definite clauses that given a bound k on the arity of predicates used in B and E, the cardinality of the enlarged bottom set is bounded above by the polynomial function p(c + 1) k , where p is the number of predicates in B; E and c is the number of constants in B [E. 1 Introduction In [5] Yam..
Logic-based machine learning using a bounded hypothesis space: the lattice structure, refinement operators and a genetic algorithm approach
No full textRich representation inherited from computational logic makes logic-based machine learning a competent method for application domains involving relational background knowledge and structured data. There is however a trade-off between the expressive power of the representation and the computational costs. Inductive Logic Programming (ILP) systems employ different kind of biases and heuristics to cope with the complexity of the search, which otherwise is intractable. Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol and Aleph). However, the structure of the search space and the properties of the refinement operators for theses systems have not been previously characterised. The contributions of this thesis can be summarised as follows: (i) characterising the properties, structure and morphisms of bounded subsumption lattice (ii) analysis of bounded refinement operators and stochastic refinement and (iii) implementation and empirical evaluation of stochastic search algorithms and in particular a Genetic Algorithm (GA) approach for bounded subsumption. In this thesis we introduce the concept of bounded subsumption and study the lattice and cover structure of bounded subsumption. We show the morphisms between the lattice of bounded subsumption, an atomic lattice and the lattice of partitions. We also show that ideal refinement operators exist for bounded subsumption and that, by contrast with general subsumption, efficient least and minimal generalisation operators can be designed for bounded subsumption. In this thesis we also show how refinement operators can be adapted for a stochastic search and give an analysis of refinement operators within the framework of stochastic refinement search. We also discuss genetic search for learning first-order clauses and describe a framework for genetic and stochastic refinement search for bounded subsumption. on. Finally, ILP algorithms and implementations which are based on this framework are described and evaluated.Open Acces
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