The Extended Importance of the Social Creation of Value in Evolutionary Processes

This is a single-authored paper delivered at the biennial European Conference on Artificial Intelligence (ECAI) in Riva del Garda, Italy 28 – 29 August 2006 and published in proceedings. The paper proposed that computational modelling be employed in order to test two processes that might hypothetically distinguish particular dimensions of human creativity. The first process is identified by the researcher as one in which the pursuit of novelty in artistic invention – especially music – tends to words the production of increasingly perceptually complex artefacts. The second process moves from a perspective orientated towards the individual artist and the individual art work’s reception to a more collective one: namely, whether cultural behaviour that tends towards novelty might find itself being reinforced by clustering of similar activities. This latter process would be one that explains why the process of “making special” – that may distinguish art in an anthropological sense – is one that forms particularly strong community bonds. These bonds between novelty seekers – which in the case of the researchers paper can be understood as musicians or artists – may reciprocally reinforce to support yet more novelty seeking. The relation of art and the new is not itself innovative. Boris Groys’ “On The New” provides a scoping of that territory. What is innovative is the proposal to use of computer simulation of individual and collective behaviour as a kind of artificial laboratory to determine the complex tendencies that animate these processes of novelty seeking and, by extension, artistic production. Computationally simulating behaviour that parallels both novelty-seeking as an individual practice (the artist) and the emergence of clusters of novelty seekers (the artistic styles) may, according to the researcher, provide us with new insights the historical evolution of creativity

A context information manager for pervasive environments

euzenat2006bInternational audienceIn a pervasive computing environment, heterogeneous devices need to communicate in order to provide services adapted to the situation of users. So, they need to assess this situation as their context. We have developed an extensible context model using semantic web technologies and a context information management component that enable the interaction between context information producer devices and context information consumer devices and as well as their insertion in an open environment

TENCompetence: Construyendo la Red Europea para el Desarrollo Continuo de Competencias

Burgos, D., Herder, E., & Olmedilla, D. (2007). TENCompetence: Construyendo la Red Europea para el Desarrollo Continuo de Competencias. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial (AEPIA).El proyecto TENCompetence (The European Network for Lifelong Competence Development) apoya a personas e instituciones europeas en el desarrollo de competencias profesionales más allá de la formación reglada oficial. El desarrollo de habilidades específicas y competencias laborales que enriquecen un curriculum y mejoran la valoración del individuo y sus capacidades profesionales centran el núcleo del proyecto. Como tal, existen dos áreas de trabajo principales: por un lado la implementación e integración de una estructura de servicios; por otro, la investigación de nuevas soluciones y técnicas a los problemas habituales en la materia. Específicamente, en referencia a la investigación, existen cuatro áreas complementarias de actuación, con diferente grado de granularidad: 1) Compartición y Administración de Recursos de Conocimiento, 2) Actividades y Unidades de Aprendizaje, 3) Programas de Desarrollo de Competencias, y 4) Redes para el Desarrollo de Competencias. Este artículo presenta los principales problemas por resolver para el desarrollo contínuo de competencias y describe las líneas de investigación definidas en el proyecto TENCompetence para abordarlos, incluyendo las principales técnicas en uso o de aplicación inmediata.This work has been sponsored by the EU project TENCompetence [www.tencompetence.org

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

Probabilistic Association Rules for Item-Based Recommender Systems

International audienceSince the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users with data, but to improve the human/computer interactions in information systems by suggesting fair items at the right time. Modeling personal preferences enables recommender systems to identify relevant subsets of items. These systems often rely on filtering techniques based on symbolic or numerical approaches in a stochastic context. In this paper, we focus on item-based collaborative filtering (CF) techniques. We show that it may be difficult to guarantee a good accuracy for the high values of prediction when ratings are not enough shared out on the rating scale. Thus, we propose a new approach combining a classic CF algorithm with an item association model to get better predictions. We deal with this issue by exploiting probalistic skewnesses in triplets of items. We validate our model by using the MovieLens dataset and get a significant improvement as regards the High MAE measure

A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval

In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail

One Approach to Knowledge Mapping for International Student Portal

* The work is partly supported by RFFI grant 08-07-00062-aKnowledge portal is an approach used to provide view of domain-specific information on the World Wide Web [13]. In this paper, we present one approach by using ontology engineering as a conceptual backbone and relationships for knowledge extracting, structuring and formalizing as a comprehensive way for building knowledge portal. For illustration of a practical ontology development of knowledge portal, the described ideas are implemented in a system design for international student service