Seismic Risk Management

Seismic risk management is a problem of many dimensions, involving multiple inputs, interactions within risk factors, criteria, alternatives and stakeholders. The deployment of this process is inherently fraught with the issues of complexity, ambiguity and uncertainty, posing extra challenges in the assessment, modelling and management stages. The complexity of earthquake impacts and the uncertain nature of information necessitate the establishment of a systematic approach to address the risk of many effects of seismic events in a reliable and realistic way. To fulfill this need, the study applies a systematic approach to the assessment and management of seismic risk and uses an integrated risk structure. The fuzzy set theory was used as a formal mathematical basis to handle uncertainties involved within risk parameters. Throughout the process, the potential impacts of an earthquake as the basic criteria for risk assessment were identified and relations between them were accommodated through a hierarchical structure. The various impacts of an earthquake are then aggregated through a composite fuzzy seismic risk index (FSRi) to screen and prioritize the retrofitting of a group of school buildings in Iran. Given the imprecise data which is the prime challenge for development of any risk model, the proposed model demonstrates a more reliable and robust methodology to handle vague and imprecise information. The significant feature of the model is its transparency and flexibility in aggregating, tracing and monitoring the risk impacts. The novelty of this study is that it serves as the first attempt of the process of a knowledge base risk-informed system for ranking and screening the retrofitting group of school buildings. The model is capable of integrating various forms of knowledge (quantitative and qualitative information) extracted from different sources (facts, algorithms, standards and experience). The outcomes of the research collectively demonstrate that the proposed system supports seismic risk management processes effectively and efficiently

Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

Multi-agent system for flood forecasting in Tropical River Basin

It is well known, the problems related to the generation of floods, their control, and management, have been treated with traditional hydrologic modeling tools focused on the study and the analysis of the precipitation-runoff relationship, a physical process which is driven by the hydrological cycle and the climate regime and that is directly proportional to the generation of floodwaters. Within the hydrological discipline, they classify these traditional modeling tools according to three principal groups, being the first group defined as trial-and-error models (e.g., "black-models"), the second group are the conceptual models, which are categorized in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to the special distribution, and finally, models that are based on physical processes, known as "white-box models" are the so-called "distributed-models". On the other hand, in engineering applications, there are two types of models used in streamflow forecasting, and which are classified concerning the type of measurements and variables required as "physically based models", as well as "data-driven models". The Physically oriented prototypes present an in-depth account of the dynamics related to the physical aspects that occur internally among the different systems of a given hydrographic basin. However, aside from being laborious to implement, they rely thoroughly on mathematical algorithms, and an understanding of these interactions requires the abstraction of mathematical concepts and the conceptualization of the physical processes that are intertwined among these systems. Besides, models determined by data necessitates an a-priori understanding of the physical laws controlling the process within the system, and they are bound to mathematical formulations, which require a lot of numeric information for field adjustments. Therefore, these models are remarkably different from each other because of their needs for data, and their interpretation of physical phenomena. Although there is considerable progress in hydrologic modeling for flood forecasting, several significant setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems, the amount of uncertainty they must deal with when trying to solve the flood forecasting problem. However, in the past decades, with the growing environment and development of the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with the stochastic nature of hydrologic events with the application of some of these techniques. Given the setbacks to hydrologic flood forecasting previously described this thesis research aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical watersheds. With the emergence of the agent technologies, the "agent-based modeling" and "multiagent systems" simulation methods have provided applications for some areas of hydro base management like flood protection, planning, control, management, mitigation, and forecasting to combat the shocks produced by floods on society; however, all these focused on evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological regime is extremely unique. In this catchment modeling environment approach, it was applied the multi-agent systems approach as a surrogate of the conventional hydrologic model to build a system that operates at the catchment level displayed with hydrometric stations, that use the data from hydrometric sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered by an organization of interacting agents whose main aim is to perform flow forecasting and awareness, and in so doing enhance the policy-making process at the watershed level. Section one of this document surveys the status of the current research in hydrologic modeling for the flood forecasting task. It is a journey through the background of related concerns to the hydrological process, flood ontologies, management, and forecasting. The section covers, to a certain extent, the techniques, methods, and theoretical aspects and methods of hydrological modeling and their types, from the conventional models to the present-day artificial intelligence prototypes, making special emphasis on the multi-agent systems, as most recent modeling methodology in the hydrological sciences. However, it is also underlined here that the section does not contribute to an all-inclusive revision, rather its purpose is to serve as a framework for this sort of work and a path to underline the significant aspects of the works. In section two of the document, it is detailed the conceptual framework for the suggested Multiagent system in support of flood forecasting. To accomplish this task, several works need to be carried out such as the sketching and implementation of the system’s framework with the (Belief-Desire-Intention model) architecture for flood forecasting events within the concept of the tropical river basin. Contributions of this proposed architecture are the replacement of the conventional hydrologic modeling with the use of multi-agent systems, which makes it quick for hydrometric time-series data administration and modeling of the precipitation-runoff process which conveys to flood in a river course. Another advantage is the user-friendly environment provided by the proposed multi-agent system platform graphical interface, the real-time generation of graphs, charts, and monitors with the information on the immediate event taking place in the catchment, which makes it easy for the viewer with some or no background in data analysis and their interpretation to get a visual idea of the information at hand regarding the flood awareness. The required agents developed in this multi-agent system modeling framework for flood forecasting have been trained, tested, and validated under a series of experimental tasks, using the hydrometric series information of rainfall, river stage, and streamflow data collected by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generación de inundaciones, su control y manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido, clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos basados en la física y la categoría de modelos impulsados por datos. Los modelos basados en la física proporcionan una descripción detallada de la dinámica relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar, se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la aplicación de algunas de estas técnicas. Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente, esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos, basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de inundaciones en el contexto de cuenca hidrográfica tropical. Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques de simulación recientes en la investigación hidrológica con modelos basados en agentes y sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo régimen hidrológico es extremadamente único. En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas multi-agente como un sustituto del modelado hidrológico convencional para construir un sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del río) capturado, almacenado y administrado por una organización de agentes interactuantes cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica. La primera sección de este documento analiza el estado del arte sobre la investigación actual en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas, métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, desde los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para subrayar los aspectos significativos de los trabajos. En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el reemplazo del modelado hidrológico convencional con el uso de sistemas multi-agente, lo que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado del proceso de precipitación-escorrentía que conduce a la inundación en el curso de un río. Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación para tener una idea visual de la información disponible con respecto a la cognición de las inundaciones. Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia, nivel del río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale

Analysis and control of FES-assisted paraplegic walking with wheel walker.

The number of people with spinal cord injury (SCI) is increasing every year and walking has been found to be the most exciting and important prospect to these patients to improve their quality of life. Many individuals with incomplete SCI have the potential to walk and everyone of them wants to try. Unfortunately up to now, there is less than one third of patients could walk again after SCI. Residual function, the orthotic support, energy expenditure, patient motivation and control technique are some of the factors that influence the walking outcome of spinal cord injured people. In this thesis, a series of studies are carried out to investigate the possibility of enhancing the performance of the functional electrical stimulation (PES) assisted paraplegic walking with wheel walker through the development and implementation of intelligent control technique and spring brake orthosis (SBO) with full utilization of the voluntary upper body effort. The main aim of this thesis is to enable individuals with complete paraplegia to walk again with maximum performance and the simplest approach as possible. Firstly, before simulation of the system can be made, it is important to select the right model to represent the actual plant. In this thesis, the development of a humanoid and wheel walker models are carried out using MSC.visualNastran4D (vN4D) software and this is integrated with Matlab Simulink® for simulation. The newly developed quadriceps and hamstrings muscle models from the series of experiments are used to represent subject muscles after comparison and validation with other two well-known muscle models are performed. Several experiments are conducted to investigate the effect of stimulation frequency and pulse-width in intermittent stimulation with isometric measurement from paraplegic subjects. The results from this work can serve as a guidance to determine the optimum stimulation parameters such as frequency and pulse-width to reduce muscle fatigue during PES application. The ability test is introduced to determine the maximum leg force that can be applied to the specific paraplegic subject during FES functional task with minimum chance of spasm and leg injury. Investigations are carried out on the control techniques implemented for FES walking with wheel walker. PID control and fuzzy logic control (FLC) are used to regulate the electrical stimulation required by the quadriceps and hamstrings muscles in order to perform the FES walking manoeuvre according to predefined walking trajectory. The body weight transfer is introduced to increase the efficiency of FES walking performance. The effectiveness of body weight transfer and control strategy to enhance the performance of FES walking and reduce stimulation pulses required is examined. Investigations are carried out on the effectiveness of spring brake orthosis (SBO) for FES assisted paraplegic walking with wheel walker. A new concept in hybrid orthotics provides solutions to the problems that affect current 'hybrid orthosis, including knee and hip flexion without relying on the withdrawal reflex or a powered actuator and foot-ground clearance without extra upper body effort. The use of SBO can also eliminate electrical stimulation pulses required by the hamstrings muscle for the same FES walking system. Further improvement of the FES walking system is achieved by introducing finite state control (FSC) to control the switching time between springs, brakes and electrical stimulation during FES assisted walking with wheel walker with the combInation of FLC to regulate the electrical stimulation required for the knee extension. The results show that FSC can be used to accurately control the switching time and improve the system robustness and stability

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Affordable identification and modelling of uncertain design specifications when introducing new technologies in space applications

When introducing new technologies in space products, both the uncertainties regarding technology feasibility and the way in which the technology affects the product development process hinder the early establishment of appropriate engineering specifications. Failing to establish product specifications during conceptual stages leads to problems discovered during later phases of the product development process, when design and process changes are the most expensive.This thesis proposes a digital holistic design platform and a method of constraints replacement for a cost- and time-efficient identification of specification uncertainties when designing space products with new technologies. The digital platform and methods have been developed and tested through industrial case studies featuring the introduction of new technologies for on-orbit applications. Most of these studies were performed in the context of, but are not limited to, the introduction of additive manufacturing.The platform and proposed constraints replacement method are based on function modeling strategies (for modeling product architecture and requirements during conceptual design phases), coupled with activity modeling strategies (for modeling the impact of product architecture on product development schedules and costs). The platform and method enable the identification and assessment of unknown uncertainties, thereby reducing the likelihood of expensive redesign processes during later development phases.Moreover, they enable the inclusion of multidisciplinary design trade-offs during conceptual stages and encourage the establishment of a culture of uncertainty seeking and effective data documentation and transfer

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains