1. The principles of empirical or inductive logic. - Venn, John, 1834-1923.
xx, 604 p.

2. Integrating Abduction and Induction - Fabrizio Riguzzi
. We propose an approach for the integration of abduction and induction in the context of Logic Programming. The integration is obtained by extending an Inductive Logic Programming system with abductive reasoning capabilities. In the resulting system, abduction is used to make assumptions in order to cover positive examples and avoid the coverage of negative ones. The assumptions generated can then be generalized in their turn. The system provides a framework for solving tasks that are difficult or impossible for most Inductive Logic Programming systems, such as: learning from incomplete knowledge, learning abductive theories, learning exceptions, learning multiple predicates and...

3. Learning Simple Phonotactics - Erik F. Tjong Kim Sang; John Nerbonne
The present paper compares stochastic learning (Hidden Markov Models) , symbolic learning (Inductive Logic Programming), and connectionist learning (Simple Recurrent Networks using backpropagation) on a single, linguistically fairly simple task, that of learning enough phonotactics to distinguish words from non-words for a simplified set of Dutch, the monosyllables. The methods are all tested using 10% reserved data as well as a comparable number of randomly generated strings. Orthographic and phonetic representations are compared. The results indicate that while stochastic and symbolic methods have little difficulty with the task, connectionist methods do.

4. Problemas en la construcción de una lógica inductiva en Rudolf Carnap - Zofío Ferrer, José Luis
Tesis doctoral inédita, leída en la Universidad Autónoma de Madrid, Facultad de Filosofía y Letras, Departamento de Lingüística, Lógica, e Historia y Filosofía de la Ciencia. Fecha de lectura: 28-11-1990

5. A system of logic, ratiocinative and inductive, being a connected view of the principles of evidence and the methods of scientific investigation. - Mill, John Stuart, 1806-1873.
622 p.

6. Inferencias inductivas y deductivas: una revisión desde la lógica clásica, la teoría de conjuntos y la cognición humana - Emiro Restrepo, Jorge

7. Topics for ILP Research - David Page; Ashwin Srinivasan
Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and computational logic. Armed with this strong foundation, ILP has been applied to important and interesting problems in the life sciences, engineering and the arts. In turn, the applications have brought into focus the need for more research into specific topics. We enumerate five of these: (1) novel search methods; (2) incorporation of explicit probabilities; (3) incorporation of special-purpose reasoners; (4) parallel execution using commodity components; and (5) enhanced human interaction. It is our hypothesis that progress in each of these areas can greatly improve...

8. Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes - C. H. Bryant; S. H. Muggleton; S. G. Oliver; D. B. Kell; P. Reiser; R. D. King

9. A ” LAYERS OF REALITY TO A WEB OF INDUCTION ” HYPOTHESIS - Afsar Abbas
It is shown that as knowledge is structured, it comes in modules. This provides different ” layers of reality ”. Each layer of reality has its own distinctive inductive logic which may differ from that of the others. All this is woven together to form a ” web of induction ” in a multidimensional space. It is the overall resilience, firmness and consistent interconnectedness of the whole web which justifies induction globally and which allows science to continue to ”read ” nature using the inductive logic. 1 Advanced knowledge of science and other disciplines is imparted through Universities. Each university...

10. Development of Methods How to Avoid the Overfitting-Effect within the GeLog-System - Gabriella Kokai
This article examines the methods how to avoid an overfitting-effect within GeLog-systems. This effect can be observed in nearly all systems of inductive concept learning, if due to false classification of examples false, especially too specific theories, are learned. There are a number or procedures, how to counter the eects of the overfitting-effect or to avoid it. This article develops criteria for the selection of those procedures. In this context, the integrability into the GeLog-system , a system of genetic inductive logic programming, is of great importance. Finally,

11. The PITA System for Logical-Probabilistic Inference - Fabrizio Riguzzi; Terrance Swift; Nova Lisboa
Probabilistic Inductive Logic Programming (PILP) is gaining interest due to its ability to model domains with complex and uncertain relations among entities. Since PILP systems generallymust solvea largenumber ofinference problems in orderto perform learning,they rely criticallyon the support of efficient inference

12. Learning morphological rules for Amharic verbs using inductive logic programming. SALTMIL-AfLaT Workshop on Language Technology for Normalisation of Less-Resourced Languages - Wondwossen Mulugeta; Michael Gasser
This paper presents a supervised machine learning approach to morphological analysis of Amharic verbs. We use Inductive Logic Programming (ILP), implemented in CLOG. CLOG learns rules as a first order predicate decision list. Amharic, an under-resourced African language, has very complex inflectional and derivational verb morphology, with four and five possible prefixes and suffixes respectively. While the affixes are used to show various grammatical features, this paper addresses only subject prefixes and suffixes. The training data used to learn the morphological rules are manually prepared according to the structure of the background predicates used for the learning process. The training...

13. Logic-based Learning in Conflict Simulation Domains - Eduardo Alonso; Daniel Kudenko
It is infeasible to specify complex Multi-Agent Systems in advance. Machine Learning techniques enable agents to learn from and adapt to the dynamic environment. So far, Reinforcement Learning methods have been extensively used in Multi-Agent Learning. However successful this approach has been, it relies on an assumption that establishes strict limits in the system's scalability: Agents are not provided with background knowledge. They learn from scratch. It is well-known, on the other hand, that domain-knowledge reduces considerably the complexity of planning problem. We propose to directly incorporate domain knowledge in the reasoning and learning processes of logic-based agents. To test...

14. Accurate Prediction of Protein Functional Class from Sequence in the M. tuberculosis and E. coli Genomes using Data Mining - Ross D. King; Andreas Karwath; Amanda Clare; Luc Dehaspe
The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on the M. tuberculosis and E. coli genomes, and identify biologically interpretable rules which predict protein functional class from only information available from the sequence. These rules predict 65% of the ORFs with no assigned function in M. tuberculosis and 24% of those in E. coli, with an estimated accuracy of...

15. Analysis and Comparative Study of Classifiers for Relational Data Mining - Vimalkumar B. Vaghela; Kalpesh H. Vandra; Nilesh K. Modi
As an important task of relational database, relational classification can directly classify the data that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. The information age has provided us with huge data repositories which cannot longer be analyzed manually. Most available existing data mining algorithms looks for pattern in a single relation. To classify data from relational database need of multi-relational classification arise which is used to analyze relational database and used to predict behavior and unknown pattern automatically which include business data, bioinformatics, pharmacology, web mining, credit card fraud detection, disease...

16. Estrategia de actividades físico recreativas para la incorporación de los adolescentes de 12 a 15 años a las actividades comunitarias en el Consejo Popular Rodas 2 - Cabrera Baró, Zeida Gicela; Carballosa Manresa, Olga Lidia; Aranzola Rodríguez, Norys Magalys
El trabajo realizado en el Consejo Popular de Rodas 2, sobre la aplicación de una estrategia de actividades físicorecreativas para la incorporación de los adolescentes a las actividades comunitarias. Aspecto imprescindible es lograr la motivación de acuerdo a los gustos, preferencias y la necesidad de que los adolescentes ocupen su tiempo libre en actividades sanas que contribuyan a la formación de su personalidad, comenzando con el diagnóstico durante 4 semanas (Septiembre) mientras que el diseño, la capacitación la validación durante 2 meses y la aplicación en el período comprendido de Enero a Julio del 2010 con el objetivo de incorporar a los adolescentes a las...

17. Block-Wise Construction of Tree-like Relational Features with Monotone Reducibility and Redundancy - Ondrej Kuzelka , Filip Zelezny
We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves monotonicity of feature reducibility and redundancy, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our block-wise approach efficiently scales to features including tens of first-order atoms, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.

18. Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties - Ondrej Kuzelka, et al.
We describe an algorithm for constructing a set of acyclic conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves a form of monotonicity of the irreducibility and relevancy feature properties, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our blockwise approach efficiently scales to features including tens of first-order literals, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.

19. Kernels on prolog proof trees: Statistical learning in the . . . - Andrea Passerini; Paolo Frasconi; Luc De Raedt

20. Expectation Maximization over Binary Decision Diagrams for Probabilistic Logic Programs - Elena Bellodi; Fabrizio Riguzzi
Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog andLogic Programs with Annotated Disjunctions. This paper proposes...

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