596 research outputs found
Agent organisations: from independent agents to virtual organisations and societies of agents
Real world applications using agent-based solutions can include many agents
that needs to communicate and interact with each other in order to meet their
objectives. In organisations; Agent open multi-agent systems, problems can
include not only the organisation of a large number of agents, but can also be
heterogeneous and of unpredictable provenance or behavior. An overview of
the alternatives for dealing with these problems is presented, highlighting the
way they try to solve or mitigate them. This approach allows the development of
complex systems in which there are agents that show very different behaviours
and that are able to adapt to unforeseen changes in the environment. This
makes it possible to simulate socio-technical or natural environments and
observe their possible evolution without the ethical considerations involved in
experimenting in real environments.This work has been developed as part of “Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla”, ID SA267P18, project financed by Junta Castilla y León, Consejería de Educación, and FEDER funds. It has been partially supported by the European Regional Development Fund (ERDF) through the Interreg Spain-Portugal V-A Program (POCTEP) under grant 0631_DIGITEC_3_E (Smart growth through the specialization of the cross-border business fabric in advanced digital technologies and blockchain.)
Comparing Agent Architectures in Social Simulation: BDI Agents versus Finite-state Machines
Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool for decision-makers to explore different strategies for managing such crisis, testing them on a simulated population; but valid results require realistic underlying models. It is therefore essential to be able to compare models using different architectures to represent the human behaviour, on objective and subjective criteria. In this paper we describe two simulations of the Australian population\u27s behaviour in bushfires: one with a finite-state machine architecture; one with a BDI architecture. We then compare these two models with respect to a number of criteria
Engineering Multi-Agent Systems: State of Affairs and the Road Ahead
The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area
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
Moral Guilt : An Agent-Based Model Analysis
International audienceIn this article we analyze the influence of a concrete moral emotion (i.e. moral guilt) on strategic decision making. We present a normal form Prisoner’s Dilemma with a moral component. We assume that agents evaluate the game’s outcomes with respect to their ideality degree (i.e. how much a given outcome conforms to the player’s moral values), based on two proposed notions on ethical preferences: Harsanyi’s and Rawls’. Based on such game, we construct and agent-based model of moral guilt, where the intensity of an agent’s guilt feeling plays a determinant role in her course of action. Results for both constructions of ideality are analyzed
SPADE 3: Supporting the New Generation of Multi-Agent Systems
[EN] Although intelligent agent-based systems have existed for several years, the progression in terms of real applications or their integration in the industry have not yet reached the expected levels. During the last two decades, many agent platforms have appeared with the aim of simplifying the development of multi-agent systems. Some of these platforms have been designed for general purposes, while others have been oriented towards specific domains. However, the lack of standards and the complexity associated with supporting such systems, among other difficulties, have hampered their generalised use. This article looks in depth at the current situation of existing agent platforms, trying to analyse their current shortcomings and their expected needs in the near future. The goal of the paper is to identify possible lines of work and some of the most crucial aspects to be considered in order to popularize the application of agent technology as a dynamic and flexible solution to current problems. Moreover, the paper presents SPADE 3, a new version of the SPADE middleware, which has been totally redesigned in order to conform to the identified challenges. Finally, a case study is proposed to illustrate how SPADE 3 is able to fulfill these challenges.This work was supported in part by the Spanish Government, under Project RTI2018-095390-B-C31-AR.Palanca Cámara, J.; Terrasa Barrena, AM.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2020). SPADE 3: Supporting the New Generation of Multi-Agent Systems. IEEE Access. 8:182537-182549. https://doi.org/10.1109/ACCESS.2020.3027357S182537182549
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