Prior knowledge for learning networks in non-probabilistic settings

AbstractCurrent learning methods for general causal networks are basically data-driven. Exploration of the search space is made by resorting to some quality measure of prospective solutions. This measure is usually based on statistical assumptions. We discuss the interest of adopting a different point of view closer to machine learning techniques. Our main point is the convenience of using prior knowledge when it is available. We identify several sources of prior knowledge and define their role in the learning process. Their relation to measures of quality used in the learning of possibilistic networks are explained and some preliminary steps for adapting previous algorithms under these new assumptions are presented

La tecnocultura y su democratización: ruido, límites y oportunidades de los Labs

Technoculture, the paradigm that emerges from the computational impact, defines a huge innovation and change movement where the concept of design reaches very radical dimensions and consequences. New identities and institutions arise. Among the latter ones, the "lab", has been used as a fuzzy descriptor of a multitude of actually rather different entities. It also has been identified as a space to accommodate and promote requests for the democratization of the current changes. The popularity of this concept requires some clarification, since even the concept of "laboratory" has exploded under the impact of the digital era. This paper contrasts these new laboratories with earlier forms of the lab organization and compares them against technological practices and new forms of innovation and research that are specific to technoculture. This allows us to identify problems and shortcomings of these new labs with respect to their ability to contribute to the democratization of technoculture. It also helps us to identify new research opportunities in the intersection between technology, design and social sciencesLa cultura que surge del impacto computacional define un gran momento de cambio e innovación en el que el concepto de diseño alcanza unas dimensiones y consecuencias muy radicales. Aparecen nuevas identidades e instituciones. Una de ellas, el lab, ha hecho fortuna como descriptor difuso de entidades muy diferentes, y también como depositaria de exigencias democratizadoras ante los cambios actuales. Esta popularidad reclama una cierta clarificación, pues el mismo concepto de "laboratorio" ha estallado bajo el impacto de lo digital. Contrastamos estos nuevos labs con los antiguos considerà¡ndolos desde los parà¡metros de la prà¡ctica tecnológica y sus formas de innovación e investigación propias de la tecnocultura. Esto nos permite identificar problemas y carencias de las nuevas organizaciones en lo que respecta a la democratización de la tecnocultura. También nos ayuda a detectar nuevas oportunidades de investigación en el cruce entre tecnología, diseño y ciencias sociale

Incremental Methods for Bayesian Network Learning

In this work we analyze the most relevant, in our opinion, algorithms for learning Bayesian Networks. We analyze methods that use goodness-of-fit tests between tentative networks and data. Within this sort of learning algorithms we distinguish batch and incremental methods. Finally, we propose a system, called BANDOLER, that incrementally learns Bayesian Networks from data and prior knowledge. The incremental fashion of the system allows to modify the learning strategy and to introduce new prior knowledge during the learning process in the light of the already learnt structure. 1 Introduction The aim of this work is twofold. On the one hand, we introduce the state of the art on learning Bayesian networks. It is intended to be a tutorial on the learning methods based on goodness-of-fit tests. We present the most significant, in our opinion, learning algorithms found in the literature, as well as the theory they are based on. On the other hand, we propose a research framework. The fiel..

Practice Exchange and Web 2.0

Abstract: The present document explores the relationships between good practice exchange through Communities of Practice (CoPs), 2.0 technologies and their consequences for eGovernment in general and, more specifically, ePractice.eu. In order to do so, we have placed the entire discussion within the larger setting of collaborative knowledge exchange and collaborative technologies, which constitute, at the same time, the core of new economic and social relationships, as well as their main facilitators

Emergence of coordination in scale-free networks

We use several models of scale-free graphs as underlying interaction graphs for a simple model of Multi-Agent Systems (MAS), and study how fast the system reaches a fixed-point, that is, the time it takes for the system to get a 90 % of the agents in the same state. The interest of these kind of graphs is in the fact that the Internet, a very plausible environment for MAS, is a scale-free graph with high clustering and ¢ knn £ , the nearest neighbor average connectivity of nodes with connectivity k, following a power-law. Our results show that different types of scale-free graphs make the system as efficient as fully connected graphs, in a clear agreement with our previous research (Artif. Intell. 141, pp. 175-181)