L'Aprenentatge en grup per projectes, una experiència enriquidora

Els canvis a la universitat s’estan produint amb un cert recel per part d’alguns a qui costa acoblar-se als dictats del nou marc que proposa l’EEES. Nosaltres portem anys treballant amb grups i per projectes, dintre dels estudis de Enginyeria del Disseny Industrial i Desenvolupament de Productes de la Universitat de Girona, en les assignatures de Projectes. La nostra experiència és molt positiva i concorda amb tot allò que es ve publicant sobre l’aprenentatge basat en grups i el model de treball sobre projectes. D’aquesta manera aconseguim una bona interacció dels estudiants amb allò que nosaltres volem que aprenguin. L’aprenentatge per projectes (APP, o PBL Project Based Learning) es un conjunt de mètodes de treball, una eina d’instrucció que ajuda tant al docent com a l’alumne en el procés d’ensenyament / aprenentatge però que requereix alguns canvis significatius respecte una classe expositiva o un tradicional treball en grup. Presentem el desenvolupament d’aquests mètodes, on a més hem introduït elements innovadors dintre el que es coneix com autogestió del temps i de l’aprenentatge

Operational Cost Reduction in WDM Networks using Lighttours

Wavelength division multiplexing (WDM) networks have been adopted as a near-future solution for the broadband Internet. In previous work we proposed a new architecture, named enhanced grooming (G+), that extends the capabilities of traditional optical routes (lightpaths). In this paper, we compare the operational expenditures incurred by routing a set of demands using lightpaths with that of lighttours. The comparison is done by solving an integer linear programming (ILP) problem based on a path formulation. Results show that, under the assumption of single-hop routing, almost 15% of the operational cost can be reduced with our architecture. In multi-hop routing the operation cost is reduced in 7.1% and at the same time the ratio of operational cost to number of optical-electro-optical conversions is reduced for our architecture. This means that ISPs could provide the same satisfaction in terms of delay to the end-user with a lower investment in the network architecture

Studies of dynamics of physical agent ecosystems

This thesis addresses the problem of learning in physical heterogeneous multi-agent systems (MAS) and the analysis of the benefits of using heterogeneous MAS with respect to homogeneous ones. An algorithm is developed for this task; building on a previous work on stability in distributed systems by Tad Hogg and Bernardo Huberman, and combining two phenomena observed in natural systems, task partition and hierarchical dominance. This algorithm is devised for allowing agents to learn which are the best tasks to perform on the basis of each agent's skills and the contribution to the team global performance. Agents learn by interacting with the environment and other teammates, and get rewards from the result of the actions they perform. This algorithm is specially designed for problems where all robots have to co-operate and work simultaneously towards the same goal. One example of such a problem is role distribution in a team of heterogeneous robots that form a soccer team, where all members take decisions and co-operate simultaneously. Soccer offers the possibility of conducting research in MAS, where co-operation plays a very important role in a dynamical and changing environment. For these reasons and the experience of the University of Girona in this domain, soccer has been selected as the test-bed for this research. In the case of soccer, tasks are grouped by means of roles. One of the most interesting features of this algorithm is that it endows MAS with a high adaptability to changes in the environment. It allows the team to perform their tasks, while adapting to the environment. This is studied in several cases, for changes in the environment and in the robot's body. Other features are also analysed, especially a parameter that defines the fitness (biological concept) of each agent in the system, which contributes to performance and team adaptability. The algorithm is applied later to allow agents to learn in teams of homogeneous and heterogeneous robots which roles they have to select, in order to maximise team performance. The teams are compared and the performance is evaluated in the games against three hand-coded teams and against the different homogeneous and heterogeneous teams built in this thesis. This section focuses on the analysis of performance and task partition, in order to study the benefits of heterogeneity in physical MAS. In order to study heterogeneity from a rigorous point of view, a diversity measure is developed building on the hierarchic social entropy defined by Tucker Balch. This is adapted to quantify physical diversity in robot teams. This tool presents very interesting features, as it can be used in the future to design heterogeneous teams on the basis of the knowledge on other teams.