Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes. To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

Assessing urban mobility through participatory scenario building and combined utility-regret assessment

La planificación de la movilidad sostenible urbana es una tarea compleja que implica un alto grado de incertidumbre debido al horizonte de planificación a largo plazo, la amplia gama de paquetes de políticas posibles, la necesidad de una aplicación efectiva y eficiente, la gran escala geográfica, la necesidad de considerar objetivos económicos, sociales y ambientales, y la respuesta del viajero a los diferentes cursos de acción y su aceptabilidad política (Shiftan et al., 2003). Además, con las tendencias inevitables en motorización y urbanización, la demanda de terrenos y recursos de movilidad en las ciudades está aumentando dramáticamente. Como consecuencia de ello, los problemas de congestión de tráfico, deterioro ambiental, contaminación del aire, consumo de energía, desigualdades en la comunidad, etc. se hacen más y más críticos para la sociedad. Esta situación no es estable a largo plazo. Para enfrentarse a estos desafíos y conseguir un desarrollo sostenible, es necesario considerar una estrategia de planificación urbana a largo plazo, que aborde las necesarias implicaciones potencialmente importantes. Esta tesis contribuye a las herramientas de evaluación a largo plazo de la movilidad urbana estableciendo una metodología innovadora para el análisis y optimización de dos tipos de medidas de gestión de la demanda del transporte (TDM). La metodología nueva realizado se basa en la flexibilización de la toma de decisiones basadas en utilidad, integrando diversos mecanismos de decisión contrariedad‐anticipada y combinados utilidad‐contrariedad en un marco integral de planificación del transporte. La metodología propuesta incluye dos aspectos principales: 1) La construcción de escenarios con una o varias medidas TDM usando el método de encuesta que incorpora la teoría “regret”. La construcción de escenarios para este trabajo se hace para considerar específicamente la implementación de cada medida TDM en el marco temporal y marco espacial. Al final, se construyen 13 escenarios TDM en términos del más deseable, el más posible y el de menor grado de “regret” como resultado de una encuesta en dos rondas a expertos en el tema. 2) A continuación se procede al desarrollo de un marco de evaluación estratégica, basado en un Análisis Multicriterio de Toma de Decisiones (Multicriteria Decision Analysis, MCDA) y en un modelo “regret”. Este marco de evaluación se utiliza para comparar la contribución de los distintos escenarios TDM a la movilidad sostenible y para determinar el mejor escenario utilizando no sólo el valor objetivo de utilidad objetivo obtenido en el análisis orientado a utilidad MCDA, sino también el valor de “regret” que se calcula por medio del modelo “regret” MCDA. La función objetivo del MCDA se integra en un modelo de interacción de uso del suelo y transporte que se usa para optimizar y evaluar los impactos a largo plazo de los escenarios TDM previamente construidos. Un modelo de “regret”, llamado “referencedependent regret model (RDRM)” (modelo de contrariedad dependiente de referencias), se ha adaptado para analizar la contribución de cada escenario TDM desde un punto de vista subjetivo. La validación de la metodología se realiza mediante su aplicación a un caso de estudio en la provincia de Madrid. La metodología propuesta define pues un procedimiento técnico detallado para la evaluación de los impactos estratégicos de la aplicación de medidas de gestión de la demanda en el transporte, que se considera que constituye una herramienta de planificación útil, transparente y flexible, tanto para los planificadores como para los responsables de la gestión del transporte. Planning sustainable urban mobility is a complex task involving a high degree of uncertainty due to the long‐term planning horizon, the wide spectrum of potential policy packages, the need for effective and efficient implementation, the large geographical scale, the necessity to consider economic, social, and environmental goals, and the traveller’s response to the various action courses and their political acceptability (Shiftan et al., 2003). Moreover, with the inevitable trends on motorisation and urbanisation, the demand for land and mobility in cities is growing dramatically. Consequently, the problems of traffic congestion, environmental deterioration, air pollution, energy consumption, and community inequity etc., are becoming more and more critical for the society (EU, 2011). Certainly, this course is not sustainable in the long term. To address this challenge and achieve sustainable development, a long‐term perspective strategic urban plan, with its potentially important implications, should be established. This thesis contributes on assessing long‐term urban mobility by establishing an innovative methodology for optimizing and evaluating two types of transport demand management measures (TDM). The new methodology aims at relaxing the utility‐based decision‐making assumption by embedding anticipated‐regret and combined utilityregret decision mechanisms in an integrated transport planning framework. The proposed methodology includes two major aspects: 1) Construction of policy scenarios within a single measure or combined TDM policy‐packages using the survey method incorporating the regret theory. The purpose of building the TDM scenarios in this work is to address the specific implementation in terms of time frame and geographic scale for each TDM measure. Finally, 13 TDM scenarios are built in terms of the most desirable, the most expected and the least regret choice by means of the two‐round Delphi based survey. 2) Development of the combined utility‐regret analysis framework based on multicriteria decision analysis (MCDA). This assessment framework is used to compare the contribution of the TDM scenario towards sustainable mobility and to determine the best scenario considering not only the objective utility value obtained from the utilitybased MCDA, but also a regret value that is calculated via a regret‐based MCDA. The objective function of the utility‐based MCDA is integrated in a land use and transport interaction model and is used for optimizing and assessing the long term impacts of the constructed TDM scenarios. A regret based model, called referente dependent regret model (RDRM) is adapted to analyse the contribution of each TDM scenario in terms of a subjective point of view. The suggested methodology is implemented and validated in the case of Madrid. It defines a comprehensive technical procedure for assessing strategic effects of transport demand management measures, which can be useful, transparent and flexible planning tool both for planners and decision‐makers.

Participatory modelling to support decision making in water management under uncertainty: Two comparative case studies in the Guadiana river basin, Spain.

A participatory modelling process has been conducted in two areas of the Guadiana river (the upper and the middle sub-basins), in Spain, with the aim of providing support for decision making in the water management field. The area has a semi-arid climate where irrigated agriculture plays a key role in the economic development of the region and accounts for around 90% of water use. Following the guidelines of the European Water Framework Directive, we promote stakeholder involvement in water management with the aim to achieve an improved understanding of the water system and to encourage the exchange of knowledge and views between stakeholders in order to help building a shared vision of the system. At the same time, the resulting models, which integrate the different sectors and views, provide some insight of the impacts that different management options and possible future scenarios could have. The methodology is based on a Bayesian network combined with an economic model and, in the middle Guadiana sub-basin, with a crop model. The resulting integrated modelling framework is used to simulate possible water policy, market and climate scenarios to find out the impacts of those scenarios on farm income and on the environment. At the end of the modelling process, an evaluation questionnaire was filled by participants in both sub-basins. Results show that this type of processes are found very helpful by stakeholders to improve the system understanding, to understand each others views and to reduce conflict when it exists. In addition, they found the model an extremely useful tool to support management. The graphical interface, the quantitative output and the explicit representation of uncertainty helped stakeholders to better understand the implications of the scenario tested. Finally, the combination of different types of models was also found very useful, as it allowed exploring in detail specific aspects of the water management problems.

Cognitive Surveillance Architecture for Scenario Understanding

Uno de los mayores retos para la comunidad científica es conseguir que las máquinas posean en un futuro la capacidad del sistema visual y cognitivo humanos, de forma que, por ejemplo, en entornos de video vigilancia, puedan llegar a proporcionar de manera automática una descripción fiable de lo que está ocurriendo en la escena. En la presente tesis, mediante la propuesta de un marco de trabajo de referencia, se discuten y plantean los pasos necesarios para el desarrollo de sistemas más inteligentes capaces de extraer y analizar, a diferentes niveles de abstracción y mediante distintos módulos de procesamiento independientes, la información necesaria para comprender qué está sucediendo en un conjunto amplio de escenarios de distinta naturaleza. Se parte de un análisis de requisitos y se identifican los retos para este tipo de sistemas en la actualidad, lo que constituye en sí mismo los objetivos de esta tesis, contribuyendo así a un modelo de datos basado en el conocimiento que permitirá analizar distintas situaciones en las que personas y vehículos son los actores principales, dejando no obstante la puerta abierta a la adaptación a otros dominios. Así mismo, se estudian los distintos procesos que se pueden lanzar a nivel interno así como la necesidad de integrar mecanismos de realimentación a distintos niveles que permitan al sistema adaptarse mejor a cambios en el entorno. Como resultado, se propone un marco de referencia jerárquico que integra las capacidades de percepción, interpretación y aprendizaje para superar los retos identificados en este ámbito; y así poder desarrollar sistemas de vigilancia más robustos, flexibles e inteligentes, capaces de operar en una variedad de entornos. Resultados experimentales ejecutados sobre distintas muestras de datos (secuencias de vídeo principalmente) demuestran la efectividad del marco de trabajo propuesto respecto a otros propuestos en el pasado. Un primer caso de estudio, permite demostrar la creación de un sistema de monitorización de entornos de parking en exteriores para la detección de vehículos y el análisis de plazas libres de aparcamiento. Un segundo caso de estudio, permite demostrar la flexibilidad del marco de referencia propuesto para adaptarse a los requisitos de un entorno de vigilancia completamente distinto, como es un hogar inteligente donde el análisis automático de actividades de la vida cotidiana centra la atención del estudio. ABSTRACT One of the most ambitious objectives for the Computer Vision and Pattern Recognition research community is that machines can achieve similar capacities to the human's visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Thus, a number of well-established scenario understanding architectural frameworks to develop applications working on a variety of environments can be found in the literature. In this Thesis, a highly descriptive methodology for the development of scene understanding applications is presented. It consists of a set of formal guidelines to let machines extract and analyse, at different levels of abstraction and by means of independent processing modules that interact with each other, the necessary information to understand a broad set of different real World surveillance scenarios. Taking into account the challenges that working at both low and high levels offer, we contribute with a highly descriptive knowledge-based data model for the analysis of different situations in which people and vehicles are the main actors, leaving the door open for the development of interesting applications in diverse smart domains. Recommendations to let systems achieve high-level behaviour understanding will be also provided. Furthermore, feedback mechanisms are proposed to be integrated in order to let any system to understand better the environment and the logical context around, reducing thus the uncertainty and noise, and increasing its robustness and precision in front of low-level or high-level errors. As a result, a hierarchical cognitive architecture of reference which integrates the necessary perception, interpretation, attention and learning capabilities to overcome main challenges identified in this area of research is proposed; thus allowing to develop more robust, flexible and smart surveillance systems to cope with the different requirements of a variety of environments. Once crucial issues that should be treated explicitly in the design of this kind of systems have been formulated and discussed, experimental results shows the effectiveness of the proposed framework compared with other proposed in the past. Two case studies were implemented to test the capabilities of the framework. The first case study presents how the proposed framework can be used to create intelligent parking monitoring systems. The second case study demonstrates the flexibility of the system to cope with the requirements of a completely different environment, a smart home where activities of daily living are performed. Finally, general conclusions and future work lines to further enhancing the capabilities of the proposed framework are presented.

A methodology to estimate uncertainty for emission projections through sensitivity analysis

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the “with measures” scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. Implications: A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.

What role does climate change play in agricultural market uncertainty? An integrated assessment taking into account market-driven adjustments

Recent studies point to climate change being one of the long-term drivers of agricultural market uncertainty. To advance in the understanding of the influence of climate change on future agricultural market developments, we compare a reference scenario for 2030 with alternative simulation scenarios that differ regarding: (1) emission scenarios; (2) climate projections; and (3) the consideration of carbon fertilization effects. For each simulation scenario, the CAPRI model provides global and EU-wide impacts of climate change on agricultural markets. Results show that climate change would considerably affect agrifood markets up to 2030. Nevertheless, market-driven adaptation strategies (production intensification, trade adjustments) would soften the impact of yield shocks on supply and demand. As a result, regional changes in production would be lower than foreseen by other studies focused on supply effects.

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

A Comparison of different Uncertainty Activation Cross-Section Data Libraries: Application to the Prediction Uncertainty in Tritium Production

T actitivity in LiPb LiPb mock-up material irradiated in Frascati: measurement and MCNP results

Assessment of energy efficiency and sustainability scenarios in the transport system

Background Energy Policy is one of the main drivers of Transport Policy. A number of strategies to reduce current energy consumption trends in the transport sector have been designed over the last decades. They include fuel taxes, more efficient technologies and changing travel behavior through demand regulation. But energy market has a high degree of uncertainty and the effectiveness of those policy options should be assessed. Methods A scenario based assessment methodology has been developed in the frame of the EU project STEPS. It provides an integrated view of Energy efficiency, environment, social and competitiveness impacts of the different strategies. It has been applied at European level and to five specific Regions. Concluding remarks The results are quite site specific dependent. However they show that regulation measures appear to be more effective than new technology investments. Higher energy prices could produce on their turn a deterioration of competitiveness and a threat for social goals.

A Comparison of Sensitivity/Uncertainty Methodologies for the Tritium Production in the HFTM/IFMIF Specimen Cells and measurements in Tritium activity in HCLL TBM mock-up LiPb

The prediction of the tritium production is required for handling procedures of samples, safety&maintenance and licensing of the International Fusion Materials Irradiation Facility (IFMIF).

A Comparison of different Uncertainty Activation Cross-Section Data Libraries and collapsed values for different neutron spectra: ADS, FISSION and FUSION

PART I:Cross-section uncertainties under differentneutron spectra. PART II: Processing uncertainty libraries

Uncertainty assessment methodologies applied to Tritium production in fusion lithium breeding blankets

- Need of Tritium production - Neutronic objectives - The Frascati experiment - Measurements of Tritium activity

Burnup Uncertainty Analysis for PWR-HFP Exercise I-1

Burn-up credit analyses are based on depletion calculations that provide an accurate prediction of spent fuel isotopic contents, followed by criticality calculations to assess keff

Analysis of Different Uncertainty Activation Cross Section Data Libraries for LWR, ads and demo Neutron Spectra

This work is aimed to present the main differences of nuclear data uncertainties among three different nuclear data libraries: EAF-2007, EAF-2010 and SCALE-6.0, under different neutron spectra: LWR, ADS and DEMO (fusion)

Isotopic uncertainty assessment due to nuclear data uncertainties in high-burnup samples.

The accurate prediction of the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. This isotopic evolution can be predicted using powerful codes and methodologies throughout irradiation as well as cooling time periods. However, in order to have a realistic confidence level in the prediction of spent fuel isotopic content, it is desirable to determine how uncertainties affect isotopic prediction calculations by quantifying their associated uncertainties.