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Nomenclatura Unesco > (11) Lógica > (1104) Lógica inductiva
(1104.01) Inducción (1104.02) Intuicionismo
(1104.03) Probabilidad (1104.99) Otras (especificar)

Mostrando recursos 1 - 20 de 2,700

1. Constraint Inductive Logic Programming and Its Application to Knowledge Discovery in Databases - Chunnian Liu; Ning Zhong; Setsuo Ohsuga
this paper, hence not very relevant to KDD. We think that the idea of CILP can be applied to the standard Empirical ILP problem and various ILP techniques: Least General Generalization Relative to Background Knowledge (rlgg) [6], Inverse Resolution [7], Top-Down Specialization [10], etc. Let us look at the Inverse Resolution technique as a case study. This method consists of backward proof steps. An ordinary resolution step takes two clauses C 1 and C 2 and resolves them to produce the resolvent C: Then an inverse resolution step takes C (could be a positive example) and C 1 (could be...

2. Noname manuscript No. (will be inserted by the editor) Explication of Inductive Probability - Patrick Maher
the date of receipt and acceptance should be inserted later Abstract Inductive probability is the logical concept of probability in ordinary lan-guage. It is vague but it can be explicated by defining a clear and precise concept that can serve some of the same purposes. This paper presents a general method for doing such an explication and then a particular explication due to Carnap. Common criticisms of Carnap’s inductive logic are examined; it is shown that most of them are spurious and the others are not fundamental. 1

3. kFOIL: Learning Simple Relational Kernels - Niels L; Andrea Passerini; Luc De Raedt; Paolo Frasconi
A novel and simple combination of inductive logic program-ming with kernel methods is presented. The kFOIL algo-rithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionaliza-tion approach. Both classification and regression tasks can be naturally handled. Experiments in applying kFOIL to well-known benchmarks in chemoinformatics show the promise of the approach.

4. Learning semantic lexicons from a part-of-speech and semantically tagged corBibliography 67 pus using inductive logic programming - Vincent Claveau; Pierrette Bouillon; James Cussens; Alan M. Frisch
This paper describes an inductive logic programming learning method designed to acquire from a corpus specific Noun-Verb (N-V) pairs—relevant in information retrieval applications to perform index expansion—in order to build up semantic lexicons based on Pustejovsky’s generative lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indi-cates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of...

5. Language Invariance and Spectrum Exchangeability in Inductive Logic - Juergen L; Je Paris; Alena Vencovska; Mims Eprint; Jeff Paris
A sufficient condition is given for a probability function in Inductive Logic (with relations of all arities) satisfying spectrum exchangeability to addition-ally satisfy Language Invariance. This condition is shown to also be necessary in the case of homogeneous probability functions.

6. Decorating proofs Extracting computational content from proofs Helmut Schwichtenberg - Realizability Interpretation; J. W. W. Diana Ratiu; Realizability Interpretation; B Ua B
Logic for inductive definitions

7. An empirical study of the use of relevance information in inductive logic programming. Machine Learning Research (to appear - Ashwin Srinivasan; Ross D. King; Michael E. Bain; Richard Dybowski; Kathryn Blackmond Laskey; James Myers; Simon Parsons
Inductive Logic Programming (ILP) systems construct models for data using domain-specific back-ground information. When using these systems, it is typically assumed that sufficient human exper-tise is at hand to rule out irrelevant background information. Such irrelevant information can, and typically does, hinder an ILP system’s search for good models. Here, we provide evidence that if expertise is available that can provide a partial-ordering on sets of background predicates in terms of relevance to the analysis task, then this can be used to good effect by an ILP system. In particu-lar, using data from biochemical domains, we investigate an incremental strategy...

8. O contador como colaborador da conscientização tributária - Fagundes Baialardi, Catiele; Alano da Rosa, Priscila; Petri, Sergio Murilo
Since the tax evasion phenomenon widespread in Brazil, it is increasingly necessary awareness of the tax payers. Within this context is the accounting professional as the most qualified to lead this process of awareness element. Thus, the aim of this study was to verify whether the contribution of accountants of accounting firms located in the neighborhood of Campinas in São José-SC-Brazil, with the tax awareness and how they influence their customers in this process. The study was classified as descriptive and inductive logic, with primary dat a source, qualitative approach and technical procedure Survey. By analyzing the responses of the questionnaire, it was found that: in most accountants principles of social and fiscal responsibility...

9. Disjunctive Bottom Set and Its Computation - Wenjin Lu; Ross King
This paper presents the concept of the disjunctive bottom set and discusses its computation. The disjunctive bottom set differs from existing extensions of the bottom set, such as kernel sets(Ray, Broda, & Russo 2003), by being the weak-est minimal single hypothesis for the whole hypothesis space. The disjunctive bottom set may be characterized in terms of minimal models. Therefore, as minimal models can be com-puted in polynomial space complexity, so can the disjunctive bottom set. We outline a flexible inductive logic program-ming framework based on the disjunctive bottom set. Com-pared with existing systems based on bottom set, such as Pro-gol...

10. Resolving Task Ordering using CILP - Wern Li Wong
In the cooking domain, multiple robotic cook agents act under the direction of a human chef to prepare dinner for a large number of people. The human chef acts as a central facili-tator/controller, assigning specific tasks to each agent in order. This paper will explore the pos-sibility of using Collaborative Inductive Logic Programming (CILP to allow the agents to work out amongst themselves, based on local knowledge, what tasks require completion with minimal input from a human controller. 1

11. Learning semantic lexicons from a part-of-speech and semantically tagged corBibliography 67 pus using inductive logic programming - Vincent Claveau; Pierrette Bouillon; James Cussens; Alan M. Frisch
This paper describes an inductive logic programming learning method designed to acquire from a corpus specific Noun-Verb (N-V) pairs—relevant in information retrieval applications to perform index expansion—in order to build up semantic lexicons based on Pustejovsky’s generative lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indi-cates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of...

12. Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems - Eric Mccreath; Arun Sharma
Many ILP systems, such as GOLEM, FOIL, and MIS, take advantage of user supplied meta-knowledge to restrict the hypothesis space. This meta-knowledge can be in the form of type information about arguments in the predicate being learned, or it can be information about whether a certain argument in the predicate is functionally dependent on the other arguments (supplied as mode information). This meta knowledge is explicitly supplied to an ILP system in addition to the data. The present paper argues that in many cases the meta knowledge can be extracted directly from the raw data. Three algorithms are presented that...

13. Discovery of First-Order Regularities in a Relational Database Using Offline Candidate Determination - Irene Weber
. In this paper, we present an algorithm for the discovery of first order clauses holding in an relational database in the framework of the nonmonotonic ILP setting [1]. The algorithm adopts the principle of offline candidate determination algorithm used for mining association rules in large transaction databases [4]. Analoguous to the measures used in mining association rules, we define a support and a confidence measure as acceptance criteria for discovered hypothesis clauses. The algorithm has been implemented in C with an interface to the relational database management system INGRES. We present and discuss the results of an experiment in...

14. Learning Function-Free Horn Expressions - Roni Khardon
The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses are examples (Learning from Entailment), models where extentional or intentional background knowledge is given to the learner (as done in Inductive Logic Programming), and the model where the reasoning performance of the learner rather than identification is of interest (Learning to Reason). We present learning algorithms for all these tasks for the class of universally quantified function free Horn expressions. The...

15. Bayesian Inductive Logic Programming - Stephen Muggleton
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lockstep development of Theory, Implementations and Applications. ILP systems have successful applications in the learning of structure-activity rules for drug design, semantic grammars rules, finite element mesh design rules and rules for prediction of protein structure and mutagenic molecules. The strong applications in ILP can be contrasted with relatively weak PAC-learning results (even highlyrestricted forms of logic programs are known to be prediction-hard). It has been recently argued that the...

16. Building Theories into Instantiation - Alan M. Frisch; C. David Page; Jr.
Instantiation orderings over formulas (the relation of one formula being an instance of another) have long been central to the study of automated deduction and logic programming, and are of rapidly-growing importance in the study of database systems and machine learning. A variety of instantiation orderings are now in use, many of which incorporate some kind of background information in the form of a constraint theory. Even a casual examination of these instantiation orderings reveals that they are somehow related, but in exactly what way? This paper presents a general instantiation ordering of which all these orderings are special cases,...

17. An Inductive Logic Programming Framework to Learn a Concept from Ambiguous Examples - Dominique Bouthinon; Henry Soldano; Atelier De Bioinformatique (abi
. We address a learning problem with the following peculiarity : we search for characteristic features common to a learning set of objects related to a target concept. In particular we approach the cases where descriptions of objects are ambiguous : they represent several incompatible realities. Ambiguity arises because each description only contains indirect information from which assumptions can be derived about the object. We suppose here that a set of constraints allows the identification of "coherent" sub-descriptions inside each object. We formally study this problem, using an Inductive Logic Programming framework close to characteristic induction from interpretations. In particular,...

18. Strongly Typed Inductive Concept Learning - P. A. Flach; Flach Giraud-Carrier; J. W. Lloyd
. In this paper we argue that the use of a language with a type system, together with higher-order facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illuminates the relationship between attribute-value learning and inductive logic programming (ILP). Individuals are represented by closed terms: tuples of constants in the case of attribute-value learning; arbitrarily complex terms in the case of ILP. To illustrate the point, we take some learning tasks from the machine learning and ILP literature and represent them in Escher, a typed, higher-order, functional logic programming language being developed...

19. Difference to Inference 1 Running Head: DIFFERENCE TO INFERENCE Difference to Inference: Teaching logical and statistical reasoning through online interactivity. - Thomas E. Malloy
Difference to Inference is an online JAVA program simulating theory testing and falsification through research design and data collection in a game format. The program, based on cognitive and epistemological principles, is designed to support the learning of thinking skills underlying deductive and inductive logic and statistical reasoning. Difference to Inference has database connectivity so that game scores can be counted as part of course grades. Difference to Inference 3 Difference to Inference: Teaching logical and statistical reasoning through online interactivity Emphasizing the active nature of information processing, Posner and Osgood (1980) proposed that computers be used to train inquiry...

20. Inductive Object-Oriented Logic Programming - Erivan Alves De Andrade; Jacques Robin
Abstract. In many of its practical applications, such as natural language processing, automatic programming, expert systems, semantic web ontologies and knowledge discovery in databases, Inductive Logic Programming (ILP) is not used to substitute but rather to complement manual knowledge acquisition.This manual acquisition is increasingly done using hybrid languages integrating objects with rules or relations. Since using a common representation language for both manually encoded and ILP learned knowledge is key to their seamless integration, this raises the issue of using such hybrid languages for induction. In this paper, we present Cigolf, an ILP system that uses the object-oriented logic language...

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