Relevant bibliographies by topics / Agent-based computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agent-based computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Agent-based computing"

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Kwang Mong Sim. "Agent-Based Cloud Computing." IEEE Transactions on Services Computing 5, no. 4 (2012): 564–77. http://dx.doi.org/10.1109/tsc.2011.52.

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Shi, Zhongzhi, He Huang, Jiewen Luo, Fen Lin, and Haijun Zhang. "Agent-based grid computing." Applied Mathematical Modelling 30, no. 7 (July 2006): 629–40. http://dx.doi.org/10.1016/j.apm.2005.06.018.

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Aleksander, Byrski. "Agent-based computing parameters tuning." Computer Science 14, no. 3 (2013): 491. http://dx.doi.org/10.7494/csci.2013.14.3.491.

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Centarowicz, Krzysztof, Maciej Paszyński, David Pardo, Tibor Bosse, and Han La Poutré. "Agent-based computing, adaptive algorithms and bio computing." Procedia Computer Science 1, no. 1 (May 2010): 1951–52. http://dx.doi.org/10.1016/j.procs.2010.04.218.

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Krzywicki, D., J. Stypka, P. Anielski, Ł. Faber, W. Turek, A. Byrski, and M. Kisiel-Dorohinicki. "Generation-free Agent-based Evolutionary Computing." Procedia Computer Science 29 (2014): 1068–77. http://dx.doi.org/10.1016/j.procs.2014.05.096.

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Lee, Wonjun, Anna Squicciarini, and Elisa Bertino. "Agent-based accountable grid computing systems." Journal of Supercomputing 65, no. 2 (January 23, 2013): 903–29. http://dx.doi.org/10.1007/s11227-013-0871-5.

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Krzywicki, D., W. Turek, A. Byrski, and M. Kisiel-Dorohinicki. "Massively concurrent agent-based evolutionary computing." Journal of Computational Science 11 (November 2015): 153–62. http://dx.doi.org/10.1016/j.jocs.2015.07.003.

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Sim, Kwang M. "Guest Editorial: Agent-based Grid computing." Applied Intelligence 25, no. 2 (October 2006): 127–29. http://dx.doi.org/10.1007/s10489-006-9649-2.

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Niazi, Muaz, and Amir Hussain. "Agent-based computing from multi-agent systems to agent-based models: a visual survey." Scientometrics 89, no. 2 (August 5, 2011): 479–99. http://dx.doi.org/10.1007/s11192-011-0468-9.

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Hu, Jun, and Chun Guan. "An Emotional Agent Model Based on Granular Computing." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/601295.

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Affective computing has a very important significance for fulfilling intelligent information processing and harmonious communication between human being and computers. A new model for emotional agent is proposed in this paper to make agent have the ability of handling emotions, based on the granular computing theory and the traditional BDI agent model. Firstly, a new emotion knowledge base based on granular computing for emotion expression is presented in the model. Secondly, a new emotional reasoning algorithm based on granular computing is proposed. Thirdly, a new emotional agent model based on granular computing is presented. Finally, based on the model, an emotional agent for patient assistant in hospital is realized, experiment results show that it is efficient to handle simple emotions.
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Dissertations / Theses on the topic "Agent-based computing"

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Cao, Junwei. "Agent-based resource management for grid computing." Thesis, University of Warwick, 2001. http://wrap.warwick.ac.uk/4172/.

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A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capability. An ideal grid environment should provide access to the available resources in a seamless manner. Resource management is an important infrastructural component of a grid computing environment. The overall aim of resource management is to efficiently schedule applications that need to utilise the available resources in the grid environment. Such goals within the high performance community will rely on accurate performance prediction capabilities. An existing toolkit, known as PACE (Performance Analysis and Characterisation Environment), is used to provide quantitative data concerning the performance of sophisticated applications running on high performance resources. In this thesis an ASCI (Accelerated Strategic Computing Initiative) kernel application, Sweep3D, is used to illustrate the PACE performance prediction capabilities. The validation results show that a reasonable accuracy can be obtained, cross-platform comparisons can be easily undertaken, and the process benefits from a rapid evaluation time. While extremely well-suited for managing a locally distributed multi-computer, the PACE functions do not map well onto a wide-area environment, where heterogeneity, multiple administrative domains, and communication irregularities dramatically complicate the job of resource management. Scalability and adaptability are two key challenges that must be addressed. In this thesis, an A4 (Agile Architecture and Autonomous Agents) methodology is introduced for the development of large-scale distributed software systems with highly dynamic behaviours. An agent is considered to be both a service provider and a service requestor. Agents are organised into a hierarchy with service advertisement and discovery capabilities. There are four main performance metrics for an A4 system: service discovery speed, agent system efficiency, workload balancing, and discovery success rate. Coupling the A4 methodology with PACE functions, results in an Agent-based Resource Management System (ARMS), which is implemented for grid computing. The PACE functions supply accurate performance information (e. g. execution time) as input to a local resource scheduler on the fly. At a meta-level, agents advertise their service information and cooperate with each other to discover available resources for grid-enabled applications. A Performance Monitor and Advisor (PMA) is also developed in ARMS to optimise the performance of the agent behaviours. The PMA is capable of performance modelling and simulation about the agents in ARMS and can be used to improve overall system performance. The PMA can monitor agent behaviours in ARMS and reconfigure them with optimised strategies, which include the use of ACTs (Agent Capability Tables), limited service lifetime, limited scope for service advertisement and discovery, agent mobility and service distribution, etc. The main contribution of this work is that it provides a methodology and prototype implementation of a grid Resource Management System (RMS). The system includes a number of original features that cannot be found in existing research solutions.
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Tang, Jia. "An agent-based peer-to-peer grid computing architecture." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060508.151716/index.html.

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Ruan, Jianhua, Han-Shen Yuh, and Koping Wang. "Spider III: A multi-agent-based distributed computing system." CSUSB ScholarWorks, 2002. https://scholarworks.lib.csusb.edu/etd-project/2249.

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The project, Spider III, presents architecture and protocol of a multi-agent-based internet distributed computing system, which provides a convenient development and execution environment for transparent task distribution, load balancing, and fault tolerance. Spider is an on going distribution computing project in the Department of Computer Science, California State University San Bernardino. It was first proposed as an object-oriented distributed system by Han-Sheng Yuh in his master's thesis in 1997. It has been further developed by Koping Wang in his master's project, of where he made large contribution and implemented the Spider II System.
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Tashakor, Ghazal. "Scalable agent-based model simulation using distributed computing on system biology." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671332.

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El modelat basat en agents és una eina informàtica molt útil que permet simular un comportament complex utilitzant regles tant a escales micro com macro. La complexitat d’aquest tipus de modelat està en la definició de les regles que tendran els agents per definir elements estructurals o els patrons de comportament estàtics i/o dinàmics. La present tesis aborda la definició de models complexos de xarxes biològiques que representen cèl·lules canceroses per obtenir comportaments sobre diferents escenaris mitjançant simulació i conèixer l’evolució del procés de metàstasi per a usuaris no-experts en sistemes de còmput. A més es desenvolupa una prova de concepte de com incorporar tècniques d’anàlisi de xarxes dinàmiques i d’aprenentatge automàtic en els models basats en agents a partir del desenvolupament d’un sistema de simulació federat per millorar el procés de presa de decisions. Per al desenvolupament d’aquesta tesi s’ha tingut que abordar, des del punt de vista de la simulació, la representació de xarxes biològiques complexes basades en grafs i investigar com integrar la topologia i funcions d’aquest tipus de xarxes interactuant amb un model basat en agents. En aquest objectiu, s’ha utilitzat el model ABM com a base per a la construcció, agrupament i classificació dels elements de la xarxa i que representen l’estructura d’una xarxa biològica complexa i escalable. La simulació d’un model complex de múltiples escales i múltiples agents, proporciona una eina útil per a que un científic, no-expert en computació, pugui executar un model complex i paramètric i utilitzar-ho com a eina d’anàlisi d’escenaris o predicció de variacions segons els diferents perfils de pacients considerats. El desenvolupament s’ha centrat en un model de tumor basat en agents que ha evolucionat des d’un model ABM simple i bé conegut, al qual se li han incorporat les variables i dinàmiques referenciades per l’Hallmarks of Cancer, fins a un models basat en grafs. Aquest model, basat en grafs, permet representar a diferents nivells d’interacció i dinàmiques dins de les cèl·lules en l’evolució d’un tumor que permet diferents graus de representacions (a nivell molecular/cel·lular). Tot això s’ha posat en funcionament en un entorn de simulació i ha creat un flux de treball (workflow) per construir una xarxa escalable complexa basada en un escenari de creixement tumoral i on s’apliquen tècniques dinàmiques per conèixer el creixement de la xarxa tumoral sobre diferents patrons. L’experimentació s’ha realitzat utilitzant l’entorn de simulació desenvolupat considerat l’execució de models per a diferents perfils de pacients, com a mostra de la seva funcionalitat, per a paràmetres d’interès per a l’expert no-informàtic com per exemple l’evolució del volum del tumor. L’entorn ha estat dissenyat per descobrir i classificar subgrafs del model de tumor basat en agents, que permetran distribuir els models en un sistema de còmput d’altes prestacions per poder analitzar escenaris complexos i/o diferents perfils de pacients amb patrons tumorals amb un alt nombre de cèl·lules canceroses en un temps reduït.
El modelado basado en agentes es una herramienta computacional muy útil que permite simular un comportamiento complejo utilizando reglas tanto en escalas micro como macro. La complejidad de este tipo de modelado radica en la definición de las reglas que tendrán los agentes para definir los elementos estructurales o los patrones de comportamiento estáticos y/o dinámicos. La presente tesis aborda la definición de modelos complejos de redes biológicas que representan células cancerosas para obtener comportamientos sobre diferentes escenarios mediante simulación y conocer la evolución del proceso de metástasis para usuarios no expertos en sistemas de cómputo. Además se desarrolla una prueba de concepto de cómo incorporar técnicas de análisis de redes dinámicas y de aprendizaje automático en los modelos basados en agentes a partir del desarrollo de un sistema de simulación federado para mejorar el proceso de toma de decisiones. Para el desarrollo de esta tesis se han tenido que abordar, desde el punto de vista de la simulación, la representación de redes biológicas complejas basadas en grafos e investigar como integrar la topología y funciones de este tipo de redes interactuando un modelo basado en agentes. En este objetivo, se ha utilizado el modelo ABM como base para la construcción, agrupamiento y clasificación de los elementos de la red y que representan la estructura de una red biológica compleja y escalable. La simulación de un modelo complejo de múltiples escalas y múltiples agentes, proporciona una herramienta útil para que un científico, no-experto en computación, pueda ejecutar un modelo complejo paramétrico y utilizarlo como herramienta de análisis de escenarios o predicción de variaciones según los diferentes perfiles de pacientes considerados. El desarrollo se ha centrado en un modelo de tumor basado en agentes que ha evolucionado desde un modelo ABM simple y bien conocido, al cual se le han incorporado las variables y dinámicas referenciadas por el Hallmarks of Cancer, a un modelo complejo basado en grafos. Este modelo, basado en grafos, se utiliza para representar a diferentes niveles de interacción y dinámicas dentro de las células en la evolución de un tumor que permite diferentes grado de representaciones (a nivel molecular/celular). Todo ello se ha puesto en funcionamiento en un entorno de simulación y se ha creado un flujo de trabajo (workflow) para construir una red escalable compleja basada en un escenario de crecimiento tumoral y donde se aplican técnicas dinámicas para conocer el crecimiento de la red tumoral sobre diferentes patrones. La experimentación se ha realizado utilizando el entorno de simulación desarrollado considerado la ejecución de modelos para diferentes perfiles de pacientes, como muestra de su funcionalidad, para calcular parámetros de interés para el experto no-informático como por ejemplo la evolución del volumen del tumor. El entorno ha sido diseñado para descubrir y clasificar subgrafos del modelo de tumor basado en agentes, que permitirá distribuir los modelos en un sistema de cómputo de altas prestaciones y así poder analizar escenarios complejos y/o diferentes perfiles de pacientes con patrones tumorales con un alto número de células cancerosas en un tiempo reducido.
Agent-based modeling is a very useful computational tool to simulate complex behavior using rules at micro and macro scales. This type of modeling’s complexity is in defining the rules that the agents will have to define the structural elements or the static and dynamic behavior patterns. This thesis considers the definition of complex models of biological networks that represent cancer cells obtain behaviors on different scenarios by means of simulation and to know the evolution of the metastatic process for non-expert users of computer systems. Besides, a proof of concept has been developed to incorporate dynamic network analysis techniques and machine learning in agent-based models based on developing a federated simulation system to improve the decision-making process. For this thesis’s development, the representation of complex biological networks based on graphs has been analyzed, from the simulation point of view, to investigate how to integrate the topology and functions of this type of networks interacting with an agent-based model. For this purpose, the ABM model has been used as a basis for the construction, grouping, and classification of the network elements representing the structure of a complex and scalable biological network. The simulation of complex models with multiple scales and multiple agents provides a useful tool for a scientist, non-computer expert to execute a complex parametric model and use it to analyze scenarios or predict variations according to the different patient’s profiles. The development has focused on an agent-based tumor model that has evolved from a simple and well-known ABM model. The variables and dynamics referenced by the Hallmarks of Cancer have been incorporated into a complex model based on graphs. Based on graphs, this model is used to represent different levels of interaction and dynamics within cells in the evolution of a tumor with different degrees of representations (at the molecular/cellular level). A simulation environment and workflow have been created to build a complex, scalable network based on a tumor growth scenario. In this environment, dynamic techniques are applied to know the tumor network’s growth using different patterns. The experimentation has been carried out using the simulation environment developed considering the execution of models for different patient profiles, as a sample of its functionality, to calculate parameters of interest for the non-computer expert, such as the evolution of the tumor volume. The environment has been designed to discover and classify subgraphs of the agent-based tumor model to execute these models in a high-performance computer system. These executions will allow us to analyze complex scenarios and different profiles of patients with tumor patterns with a high number of cancer cells in a short time.
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Bicak, Mesude. "Agent-based modelling of decentralized ant behaviour using high performance computing." Thesis, University of Sheffield, 2011. http://etheses.whiterose.ac.uk/1392/.

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Ant colonies are complex biological systems that respond to changing conditions in nature by solving dynamic problems. Their ability of decentralized decision-making and their self-organized trail systems have inspired computer scientists since 1990s, and consequently initiated a class of heuristic search algorithms, known as ant colony optimization (ACO) algorithms. These have proven to be very effective in solving combinatorial optimisation problems, especially in the field of telecommunication. The major challenge in social insect research is understanding how colony-level behaviour emerges from individual interactions. Models to date focus on simple pheromone usage with mathematically devised behaviour, which deviates largely from the real ant behaviour. Furthermore, simulating large-scale behaviour at the individual level is a difficult computational challenge; hence models fail to simulate realistic colony sizes and dimensions for foraging environments. In this thesis, FLAME, an agent-based modelling (ABM) framework capable of producing parallelisable models, was used as the modelling platform and simulations were performed on a High Performance Computing (HPC) grid. This enabled large-scale simulations of complex models to be run in parallel on a grid, without compromising on the time taken to attain results. Furthermore, the advanced features of the framework, such as dynamic creation of agents during a simulation, provided realistic grounds for modelling pheromones and the environment. ABM approach through FLAME was utilized to improve existing models of the Pharaoh's ants (Monomorium pharaonis) focusing on their foraging strategies. Based on related biological research, a number of hypotheses were further tested, which were: (i) the ability of the specialist ‘U-turner' ants in trail maintenance, (ii) the trail choices performed at bifurcations, and (iii) the ability of ants to deposit increased concentrations of pheromones based on food quality. Heterogeneous colonies with 7% U-turner ant agents were further shown to perform significantly better in foraging compared to homogeneous colonies. Furthermore, laying pheromones with a higher intensity based on food quality was shown to be beneficial for the Pharaoh's ant colonies in switching to more rewarding trails. The movement of the Pharaoh's ants in unexplored areas (without pheromones) was also investigated by conducting biological experiments. Video tracking was used to extract movement vectors from the recordings of experiments and the data obtained was subject to statistical analysis in order to devise parameters for ant movement in the models developed. Overall, this research makes contributions to biology and computer science research by: (i) utilizing ABM and HPC via FLAME to reduce technological challenges, (ii) further validating existing hypotheses through realistic models, (iii) developing a video tracking system to acquire experimental data, and (iv) discussing potential applications to emergent telecommunication and networking problems.
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Gusukuma, Luke. "GPU Based Large Scale Multi-Agent Crowd Simulation and Path Planning." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78098.

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Crowd simulation is used for many applications including (but not limited to) videogames, building planning, training simulators, and various virtual environment applications. Particularly, crowd simulation is most useful for when real life practices wouldn't be practical such as repetitively evacuating a building, testing the crowd flow for various building blue prints, placing law enforcers in actual crowd suppression circumstances, etc. In our work, we approach the fidelity to scalability problem of crowd simulation from two angles, a programmability angle, and a scalability angle, by creating new methodology building off of a struct of arrays approach and transforming it into an Object Oriented Struct of Arrays approach. While the design pattern itself is applied to crowd simulation in our work, the application of crowd simulation exemplifies the variety of applications for which the design pattern can be used.
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Mengistu, Dawit. "Multi-Agent Based Simulations in the Grid Environment." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00371.

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The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources. Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time. Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour. For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance. Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications. It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid. It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions. Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation. We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid. Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.
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Murdock, J. William. "Self-improvment through self-understanding : model-based reflection for agent adaptation." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8225.

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Karimian, Kimia. "BioCompT - A Tutorial on Bio-Molecular Computing." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367943120.

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Liu, Zhengchun. "Modeling and simulation for healthcare operations management using high performance computing and agent-based model." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/392743.

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Los servicios hospitalarios de urgencias (SU) son servicios altamente integrados que gestionan las necesidades primarias de los pacientes que llegan sin cita previa y en condiciones inciertas. En este contexto, el análisis y la gestión de flujos de pacientes ejercen un papel clave en el desarrollo de las políticas y herramientas de decisión para mejorar la actuación global del sistema. Pese a esto, los mismos flujos de pacientes en un SU son considerados muy complejos debido a los diferentes caminos que pueden tomar los pacientes y a la inherente incerteza y variabilidad de los servicios de salud. Debido a la complejidad y al papel crucial de un SU en el sistema sanitario, la habilidad de representar, simular y predecir el rendimiento de un SU tiene un valor incalculable para quien toma decisiones para resolver los problemas de la gestión de las operaciones. Una manera a percatarse de las consecuencias es mediante el modelado y la simulación. El objetivo general de este estudio es desarrollar herramientas para entender mejora la complejidad (explicar), evaluar la política (predecir) y mejorar la eficiencia (optimizar) de unidades de SU. Las dos aportaciones principales son: (1) Un modelo basado en agentes para predecir y analizar cuantitativamente el complejo comportamiento de los servicios de urgencias. E objetivo de este modelo es captar la asociación no lineal entre las funciones de nivel macro y el comportamiento a nivel micro con el objetivo de comprender mejor el cuello de botella del rendimiento de los SU y proporcionar la capacidad de cuantificar este rendimiento en una condición dada. El modelo fue construido en colaboración con el personal de asistencia sanitaria en un SU típica y ha sido implementado en el entorno de modelado NetLogo. Se proporcionan casos de estudio para presentar algunas capacidades del simulador que analizan cuantitativamente el comportamiento del SU así como el soporte a la toma de decisiones. (2) Una metodología de simulación basada en la optimización para el calibrado de los parámetros del modelo en condiciones de escasez de datos. Para conseguir una alta fidelidad y credibilidad en la realización de la predicción y exploración del sistema actual con modelos de simulaciones se ha de aplicar en primer lugar una calibración rigurosa y un procedimiento de validación. No obstante, una de las cuestiones clave en el calibrado es la adquisición de información de una fuente válida para el sistema destino. El objetivo de este trabajo es desarrollar un método sistemático para calibrar automáticamente un modelo genérico de un servicio de urgencias con datos incompletos. El método de calibrado propuesto permite a los usuarios de la simulación calibrar el modelo genérico para la simulación de los propios sistemas sin involucrarse en el modelo. Las técnicas de computación de alto rendimiento se utilizaron para buscar el conjunto óptimo de parámetros de manera eficiente. Creemos que una herramienta de calibrado automático publicado juntamente con un modelo genérico de un SU es prometedor para la promoción de la aplicación de la simulación en los estudios de SU. Además, la integración de técnicas de simulación de un SU i optimación podrían también ser utilizada para la optimización sistemática de un SU. A partir de la simulación de los servicios de urgencias, nuestros esfuerzos probaron la viabilidad y la idoneidad de la utilización del modelo de simulación y técnicas basadas en agentes para el estudio del sistema de salud.
Hospital based emergency departments (EDs) are highly integrated service units to primarily handle the needs of the patients arriving without prior appointment, and with uncertain conditions. In this context, analysis and management of patient flows play a key role in developing policies and decision tools for overall performance improvement of the system. However, patient flows in EDs are considered to be very complex because of the different pathways patients may take and the inherent uncertainty and variability of healthcare processes. Due to the complexity and crucial role of an ED in the healthcare system, the ability to accurately represent, simulate and predict performance of ED is invaluable for decision makers to solve operations management problems. One way to realize this requirement is by modeling and simulation. Armed with the ability to execute a compute-intensive model and analyze huge datasets, the overall goal of this study is to develop tools to better understand the complexity (explain), evaluate policy (predict) and improve efficiencies (optimize) of ED units. The two main contributions are: (1) An agent-based model for quantitatively predicting and analyzing the complex behavior of emergency departments. The objective of this model is to grasp the non-linear association between macro-level features and micro-level behavior with the goal of better understanding the bottleneck of ED performance and provide ability to quantify such performance on defined condition. The model was built in collaboration with healthcare staff in a typical ED and has been implemented in a NetLogo modeling environment. In order to validate its adaptivity, the presented model has been calibrated to emulate a real ED in Spain, simulation results have proven the feasibility and ideality of using agent-based model & simulation techniques to study the ED system. Case studies are provided to present some capabilities of the simulator on quantitively analyzing ED behavior and supporting decision making. (2) A simulation and optimization based methodology for calibrating model parameters under data scarcity. To achieve high fidelity and credibility in conducting prediction and exploration of the actual system with simulation models, a rigorous calibration and validation procedure should firstly be applied. However, one of the key issues in calibration is the acquisition of valid source information from the target system. The aim of this contribution is to develop a systematic method to automatically calibrate a general emergency department model with incomplete data. The proposed calibration method enables simulation users to calibrate the general model for simulating their system without the involvement of model developers. High performance computing techniques were used to efficiently search for the optimal set of parameters. The case study indicates that the proposed method appears to be capable of properly calibrating and validating the simulation model with incomplete data. We believe that an automatic calibration tool released with a general ED model is promising for promoting the application of simulation in ED studies. In addition, the integration of the ED simulator and optimization techniques could be used for ED systematic performance optimization as well. Starting from simulating the emergency departments, our efforts proved the feasibility and ideality of using agent-based model methods to study healthcare systems.
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Books on the topic "Agent-based computing"

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Duarte, Bouça, and Gafagnão Amaro, eds. Agent-based computing. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Mangina, Eleni, Javier Carbo, and José M. Molina. Agent-Based Ubiquitous Computing. Paris: Atlantis Press, 2010. http://dx.doi.org/10.2991/978-94-91216-31-2.

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Mangina, Eleni. Agent-Based Ubiquitous Computing. Paris: Atlantis Press, 2010.

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Niazi, Muaz A., and Amir Hussain. Cognitive Agent-based Computing-I. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-3852-2.

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Griffiths, Nathan, and Kuo-Ming Chao, eds. Agent-Based Service-Oriented Computing. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84996-041-0.

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Cranefield, Stephen, and Insu Song, eds. Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35612-4.

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Niazi, Muaz A. Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods. Dordrecht: Springer Netherlands, 2013.

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Zili, Zhang. Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving. Berlin: Springer, 2004.

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1959-, Padget Julian A., ed. Collaboration between human and artificial societies: Coordination and agent-based distributed computing. New York: Springer, 1999.

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Cranefield, Stephen. Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing: International Workshops, PRIMA 2011, Wollongong, Australia, November 14, 2011 Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Agent-based computing"

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Byrski, Aleksander, and Marek Kisiel-Dorohinicki. "Agent-Based Computing." In Evolutionary Multi-Agent Systems, 31–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51388-1_2.

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Jennings, Nicholas R. "Agent-Based Computing." In Intelligent Information Processing, 17–30. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35602-0_3.

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Zhang, Minjie, Jia Tang, and John Fulcher. "Agent-Based Grid Computing." In Studies in Computational Intelligence, 439–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78293-3_11.

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Jagtap, Manasi, Ankita Govekar, Nimita Joshi, Shambhavi Joshi, and Sandhya Arora. "AI-Based Interview Agent." In ICT Infrastructure and Computing, 527–35. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5331-6_54.

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Luck, Michael, Peter McBurney, and Jorge Gonzalez-Palacios. "Agent-Based Computing and Programming of Agent Systems." In Lecture Notes in Computer Science, 23–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678823_2.

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Vanhée, Loïs, Jacques Ferber, and Frank Dignum. "Agent-Based Evolving Societies." In Advances in Intelligent Systems and Computing, 283–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39829-2_25.

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North, Michael J., and Cynthia S. Hood. "Users Matter: A Multi-agent Systems Model of High Performance Computing Cluster Users." In Multi-Agent and Multi-Agent-Based Simulation, 99–113. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32243-6_9.

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Niazi, Muaz A., and Amir Hussain. "Introduction." In Cognitive Agent-based Computing-I, 1–14. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3852-2_1.

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Niazi, Muaz A., and Amir Hussain. "A Unified Framework." In Cognitive Agent-based Computing-I, 15–20. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3852-2_2.

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Niazi, Muaz A., and Amir Hussain. "Complex Adaptive Systems." In Cognitive Agent-based Computing-I, 21–32. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3852-2_3.

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Conference papers on the topic "Agent-based computing"

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Chou, Yu-Cheng, David Ko, and Harry H. Cheng. "Mobile Agent Based Autonomic Dynamic Parallel Computing." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87750.

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Parallel computing is widely adotped in scientific and engineering applications to enhance the efficiency. Moreover, there are increasing research interests focusing on utilizing distributed networked computers for parallel computing. The Message Passing Interface (MPI) standard was designed to support portability and platform independence of a developed parallel program. However, the procedure to start an MPI-based parallel computation among distributed computers lacks autonomicity and flexibility. This article presents an autonomic dynamic parallel computing framework that provides autonomicity and flexibility that are important and necessary to some parallel computing applications involving resource constrained and heterogeneous platforms. In this framework, an MPI parallel computing environment consisting of multiple computing entities is dynamically established through inter-agent communications using the IEEE Foundation for Intelligent Physical Agents (FIPA) compliant Agent Communication Language (ACL) messages. For each computing entity in the MPI parallel computing environment, a load-balanced MPI program C source code along with the MPI environment configuration statements are dynamically composed as a mobile agent code. A mobile agent wrapping the mobile agent code is created and sent to the computing entity where the mobile agent code is retrieved and interpretively executed. An example of autonomic parallel matrix multiplication is given to demonstrate the self-configuration and self-optimization properties of the presented framework.
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"Workshop on agent based computing V." In 2008 International Multiconference on Computer Science and Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/imcsit.2008.4747209.

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Hafez, Mohamed Galal, and Mohamed Shaheen Elgamel. "Agent-Based Cloud Computing: A Survey." In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2016. http://dx.doi.org/10.1109/ficloud.2016.48.

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Liu, Feng, Zhongwei Xu, and Qi Wang. "Grid Computing Monitoring Based on Agent." In 2006 First International Symposium on Pervasive Computing and Applications. IEEE, 2006. http://dx.doi.org/10.1109/spca.2006.297571.

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Wajid, Usman, and Cesar A. Marin. "Agent-Based Computing for Enterprise Collaboration." In 2009 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE). IEEE, 2009. http://dx.doi.org/10.1109/wetice.2009.62.

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Puusepp, Renee. "Agent-based models for computing circulation." In ACADIA 2014: Design Agency. ACADIA, 2014. http://dx.doi.org/10.52842/conf.acadia.2014.043.

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Cabri, Giacomo. "Agent-Based Computing for Enterprise Collaboration--Agent-Oriented Workflows and Services." In 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE'06). IEEE, 2006. http://dx.doi.org/10.1109/wetice.2006.17.

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Li, Xiong, Yujin Wu, Kai Wang, and Zongchang Xu. "Concurrent Negotiations for Agent-Based Grid Computing." In 2006 5th IEEE International Conference on Cognitive Informatics. IEEE, 2006. http://dx.doi.org/10.1109/coginf.2006.365673.

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Collier, R. W., M. J. O'Grady, G. M. P. O'Hare, C. Muldoon, D. Phelan, R. Strahan, and Y. Tong. "Self-organisation in agent-based mobile computing." In Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004. IEEE, 2004. http://dx.doi.org/10.1109/dexa.2004.1333567.

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Szpryngier, P., and M. Matuszek. "Selected security aspects of agent-based computing." In 2010 International Multiconference on Computer Science and Information Technology (IMCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/imcsit.2010.5679919.

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Reports on the topic "Agent-based computing"

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Dean, Michael. Agent-Based Computing Integration and Testing. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada462293.

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Macdonald, John S. Agent Based Computing and Effective Self-Synchronization in Netted Warfare. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada415987.

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Pollack, Martha E. Development of a Formal Theory of Agent-Based Computing for System Evaluation and System-Design Guidance. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada424483.

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Kondratenko, Larysa O., Hanna T. Samoylenko, Arnold E. Kiv, Anna V. Selivanova, Oleg I. Pursky, Tetyana O. Filimonova, and Iryna O. Buchatska. Computer simulation of processes that influence adolescent learning motivation. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4452.

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In order for the learning process to always retain personal value for the learner, it is necessary that his or her motivation be maintained through an awareness of his or her purpose and goals. This article presents a local model (at the individual object level) of enhancing external motivation, which give to determine students’ efforts to get rewards. The concept of this model based on describing the behavior of agents (in our case students). The characteristics of the phenomenon in the motivation of learning at different stages of adolescent development are analyzed. The problem of computer modeling of educational processes with the help of agent modeling on the example of studying student motivation is considered. Internal and external factors that may strengthen or weaken the adolescent’s motivation to study have been studied. The expediency of using information technologies of agent modeling to study the dynamics of strengthening or weakening student motivation is substantiated. Using the AnyLogic Cloud computing environment the change of dynamics of strengthening of motivation of teenagers on an example of model of strengthening of external motivation is defined.
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