Social Presence Approach Within the Question and Answering eLearning Model: An Experiment with a Multi-Agent System

The model of Questions Answering (Q&A) for eLearning is based on collaborative learning through questions that are posed by students and their answers to that questions which are given by peers, in contrast with the classical model in which students ask questions to the teacher only. In this proposal we extend the Q&A model including the social presence concept and a quantitative measure of it is proposed; besides it is considered the evolution of the resulting Q&A social network after the inclusion of the social presence and taking into account the feedback on questions posed by students and answered by peers. The social network behaviorwas simulated using a Multi-Agent System to compare the proposed social presence model with the classical and the Q&A models

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Simulation and optimization of IGCC technique for power generation and hydrogen production by using lignite Thar coal and cotton stalk

The purpose of this study was to simulate and to optimize integrated gasification for combine cycle (IGCC) for power generation and hydrogen (H2) production by using low grade Thar lignite coal and cotton stalk. Lignite coal is abundant of moisture and ash content, the idea of addition of cotton stalk is to increase the mass of combustible material per mass of feed use for the process, to reduce the consumption of coal and to increase the cotton stalk efficiently for IGCC process. Aspen plus software is used to simulate the process with different mass ratios of coal to cotton stalk and for optimization: process efficiencies, net power generation and H2 production etc. are considered while environmental hazard emissions are optimized to acceptance level. With the addition of cotton stalk in feed, process efficiencies started to decline along with the net power production. But for H2 production, it gave positive result at start but after 40% cotton stalk addition, H2 production also started to decline. It also affects negatively on environmental hazard emissions and mass of emissions/ net power production increases linearly with the addition of cotton stalk in feed mixture. In summation with the addition of cotton stalk, overall affects seemed to negative. But the effect is more negative after 40% cotton stalk addition so it is concluded that to get maximum process efficiencies and high production less amount of cotton stalk addition in feed is preferable and the maximum level of addition is estimated to 40%. Gasification temperature should keep lower around 1140 °C and prefer technique for studied feed in IGCC is fluidized bed (ash in dry form) rather than ash slagging gasifier

Simulation and optimization tools in paper machine concept design

The last decade has shown that the global paper industry needs new processes and products in order to reassert its position in the industry. As the paper markets in Western Europe and North America have stabilized, the competition has tightened. Along with the development of more cost-effective processes and products, new process design methods are also required to break the old molds and create new ideas. This thesis discusses the development of a process design methodology based on simulation and optimization methods. A bi-level optimization problem and a solution procedure for it are formulated and illustrated. Computational models and simulation are used to illustrate the phenomena inside a real process and mathematical optimization is exploited to find out the best process structures and control principles for the process. Dynamic process models are used inside the bi-level optimization problem, which is assumed to be dynamic and multiobjective due to the nature of papermaking processes. The numerical experiments show that the bi-level optimization approach is useful for different kinds of problems related to process design and optimization. Here, the design methodology is applied to a constrained process area of a papermaking line. However, the same methodology is applicable to all types of industrial processes, e.g., the design of biorefiners, because the methodology is totally generalized and can be easily modified.

A formalization for multi-agent decision support in cooperative environments. A framework for situated agents

La tesis propone un marco de trabajo para el soporte de la toma de decisiones adecuado para soportar la ejecución distribuida de acciones cooperativas en entornos multi-agente dinámicos y complejos. Soporte para la toma de decisiones es un proceso que intenta mejorar la ejecución de la toma de decisiones en escenarios cooperativos. Este proceso ocurre continuamente en la vida diaria. Los humanos, por ejemplo, deben tomar decisiones acerca de que ropa usar, que comida comer, etc. En este sentido, un agente es definido como cualquier cosa que está situada en un entorno y que actúa, basado en su observación, su interpretación y su conocimiento acerca de su situación en tal entorno para lograr una acción en particular.Por lo tanto, para tomar decisiones, los agentes deben considerar el conocimiento que les permita ser consientes en que acciones pueden o no ejecutar. Aquí, tal proceso toma en cuenta tres parámetros de información con la intención de personificar a un agente en un entorno típicamente físico. Así, el mencionado conjunto de información es conocido como ejes de decisión, los cuales deben ser tomados por los agentes para decidir si pueden ejecutar correctamente una tarea propuesta por otro agente o humano. Los agentes, por lo tanto, pueden hacer mejores decisiones considerando y representando apropiadamente tal información. Los ejes de decisión, principalmente basados en: las condiciones ambientales, el conocimiento físico y el valor de confianza del agente, provee a los sistemas multi-agente un confiable razonamiento para alcanzar un factible y exitoso rendimiento cooperativo.Actualmente, muchos investigadores tienden a generar nuevos avances en la tecnología agente para incrementar la inteligencia, autonomía, comunicación y auto-adaptación en escenarios agentes típicamente abierto y distribuidos. En este sentido, esta investigación intenta contribuir en el desarrollo de un nuevo método que impacte tanto en las decisiones individuales como colectivas de los sistemas multi-agente. Por lo tanto, el marco de trabajo propuesto ha sido utilizado para implementar las acciones concretas involucradas en el campo de pruebas del fútbol robótico. Este campo emula los juegos de fútbol real, donde los agentes deben coordinarse, interactuar y cooperar entre ellos para solucionar tareas complejas dentro de un escenario dinámicamente cambiante y competitivo, tanto para manejar el diseño de los requerimientos involucrados en las tareas como para demostrar su efectividad en trabajos colectivos. Es así que los resultados obtenidos tanto en el simulador como en el campo real de experimentación, muestran que el marco de trabajo para el soporte de decisiones propuesto para agentes situados es capaz de mejorar la interacción y la comunicación, reflejando en un adecuad y confiable trabajo en equipo dentro de entornos impredecibles, dinámicos y competitivos. Además, los experimentos y resultados también muestran que la información seleccionada para generar los ejes de decisión para situar a los agentes, es útil cuando tales agentes deben ejecutar una acción o hacer un compromiso en cada momento con la intención de cumplir exitosamente un objetivo colectivo. Finalmente, algunas conclusiones enfatizando las ventajas y utilidades del trabajo propuesto en la mejora del rendimiento colectivo de los sistemas multi-agente en situaciones tales como tareas coordinadas y asignación de tareas son presentadas.

A Multi-agent Architecture with Distributed Coordination for an Autonomous Robot

Aquest treball proposa una nova arquitectura de control amb coordinació distribuïda per a un robot mòbil (ARMADiCo). La metodologia de coordinació distribuïda consisteix en dos passos: el primer determina quin és l'agent que guanya el recurs basat en el càlcul privat de la utilitat i el segon, com es fa el canvi del recurs per evitar comportaments abruptes del robot. Aquesta arquitectura ha estat concebuda per facilitar la introducció de nous components hardware i software, definint un patró de disseny d'agents que captura les característiques comunes dels agents. Aquest patró ha portat al desenvolupament d'una arquitectura modular dins l'agent que permet la separació dels diferents mètodes utilitzats per aconseguir els objectius, la col·laboració, la competició i la coordinació de recursos. ARMADiCo s'ha provat en un robot Pioneer 2DX de MobileRobots Inc.. S'han fet diversos experiments i els resultats han demostrat que s'han aconseguit les característiques proposades per l'arquitectura.

Multi-agent building control in shared environment

Multi-agent systems have been adopted to build intelligent environment in recent years. It was claimed that energy efficiency and occupants' comfort were the most important factors for evaluating the performance of modem work environment, and multi-agent systems presented a viable solution to handling the complexity of dynamic building environment. While previous research has made significant advance in some aspects, the proposed systems or models were often not applicable in a "shared environment". This paper introduces an ongoing project on multi-agent for building control, which aims to achieve both energy efficiency and occupants' comfort in a shared environment.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.