Supporting multi-agent systems life cycle by integrating protégé and prometheus

In this paper, an approach aiming to support the complete multi-agent systems (MAS) life cycle is proposed. Two existing and widely accepted tools, Protege Ontology Editor and Knowledge-Base Framework and Prometheus Development Kit, are integrated, offering a general sequence of steps facilitating application creation. It seems reasonable to integrate all traditional software development stages into one single methodology, which can provide a general approach for MAS creation, starting with problem definition and resulting in program coding, deployment and maintenance. The approach is successfully being applied to situation assessment issues, which has concluded in an agent-based decision-support system for environmental impact evaluation. An example is offered to evaluate the impact of environmental parameters upon human health in Spanish region Castilla-La Mancha, in general, and in the city of Albacete, in particular

Evaluation of environmental impact upon human health with decimas framework

The article is dedicated to the problem of decision making in complex systems. Application of a novel interdisciplinary approach, which widely use intelligent agents is offered. The principal ideas of the novel approach are embodied into the DeciMaS framework, that offers a logical set of stages oriented to creation of decision support systems for complex problem management. The components of the DeciMaS framework and the way in which they are organized are introduced. Design and implementation of the system are discussed. The article demonstrates how the initial information is transformed into knowledge. Impact assessment upon human health evaluation is the case study, which is resolved by DeciMas framework. It includes creation of the meta-ontology. In addition, a multi-agent architecture for a decision support system is introduced. The sequence of the steps for the DeciMaS framework design with Prometheus Development Kit and its implementation with JACK Development Environment are presented as well. Finally, data and experiment results of data modeling, simulation, impact assessment, and decision generation are discussed

Agent Based Modeling and Simulation Framework for Supply Chain Risk Management

This research develops a flexible agent-based modeling and simulation (ABMS) framework for supply chain risk management with significant enhancements to standard ABMS methods and supply chain risk modeling. Our framework starts with the use of software agents to gather and process input data for use in our simulation model. For our simulation model, we extend an existing mathematical framework for discrete event simulation (DES) to ABMS and then implement the concepts of variable resolution modeling from the DES domain to ABMS and provide further guidelines for aggregation and disaggregation of supply chain models. Existing supply chain risk management research focuses on consumable item supply chains. Since the Air Force supply chain contains many reparable items, we fill this gap with our risk metrics framework designed for reparable item supply chains, which have greater complexity than consumable item supply chains. We present new metrics, along with existing metrics, in a framework for reparable item supply chain risk management and discuss aggregation and disaggregation of metrics for use with our variable resolution modeling

Modeling and implementing an agent-based environmental health impact decision support system

This paper presents an approach to the creation of an agent-based system for the assessment of environmental impact upon human health. As indicators of the environmental impact water pollution, indexes of traffic and industrial activity, wastes and solar radiation are assumed. And as human health indicator morbidity is taken. All the data comprise multiple heterogeneous data repositories. The system is logically and functionally divided into three layers, solving the tasks of information fusion, pattern discovery and decision support making, respectively. The outcomes of the system design phase under Prometheus methodology and the complete characteristics of the agents forming the proposal are discussed. The discovered patterns are used as a foundation for real-time decision making, which is of great importance for adequate and effective management by responsible governmental authorities

Using agent-based modelling and simulation to model performance measurement in healthcare

One of the priority areas of the UK healthcare system is urgent and emergency care, especially accident and emergency departments (A&E departments). Currently, there is much interest in studying the unintended consequences of the current UK healthcare performance system. Simulation modelling has been proved to be a useful tool for modelling different aspects of the healthcare systems, particularly those related to the performance of A&E departments. Most of the available literature on modelling A&E departments focus on supporting operational decision-making and planning in specific healthcare units to study particular problems such as staff scheduling, resource utilisation, and waiting time issues. That is, most simulation studies focus on analysing how different configurations of healthcare systems affect their performance. However, to our knowledge, few simulation studies focus on explaining how human behaviour affects the performance of the system, and very few have studied how, in turn, performance targets set for A&E departments affect human behaviour in healthcare systems. Some aspects of human behaviour have been incorporated within existing simulation models, though with limitations. In fact, most studies have aimed to study patients’ behaviour, and few have included some aspects of the behaviour of clinical staff. Here we consider how to model clinician behaviour in relation to the performance of A&E departments. This thesis presents an exploratory study of the use of agent-based modelling and simulation (ABMS) and discrete event simulation (DES) to demonstrate how to model clinician behaviour within an A&E department and how that behaviour is related to waiting time performance. Clinical behaviour, incorporated in the simulation models developed here, employs a framework called PECS that assumes that behaviour is influenced by Physical (P), Emotional (E), Cognitive (C) and Social (S) factors. A discussion of the advantages and limitations of the use of ABMS and DES to model such behaviour is included. The findings of this research demonstrate that ABMS is well suited to simulate human behaviour in an A&E department. However, it is not explicitly designed to model processes of complex operational and queue-based systems such as accident and emergency departments. In addition, this research work also demonstrates that DES is an adequate tool for modelling A&E’s processes and patient flows, that can, in fact, incorporate different aspects of human behaviour. Furthermore, the process of modelling human behaviour in DES is complex because, though most DES software allows the representation of reactive behaviour, they make it difficult to model other types of human behaviour The main contributions of this thesis are: 1) a comparison and evaluation of how suitable ABMS and DES are for modelling clinical behaviour, 2) an approach to model the relationship between human behaviour and waiting time performance, considering four aspects of human behaviour (physical, emotional, cognitive and social)

Sistema Inteligente de Ayuda a la Decisión para el Diagnóstico Temprano de la Meningitis

Fecha de lectura de Tesis Doctoral: 18 febrero 2020La meningitis es una enfermedad pandémica que sufren muchos países poco desarrollados, principalmente debido a la falta de recursos económicos. El tipo más grave de meningitis, la enfermedad meningocócica, exige una atención médica inmediata ya que retrasos en su diagnóstico aumentan el riesgo de mortalidad. Esta tesis propone un sistema inteligente de ayuda a la decisión, basado en una arquitectura de Sistemas Multiagente, con el objetivo de ayudar a los médicos en las diferentes etapas del diagnóstico precoz de la meningitis, a través, principalmente, de síntomas observables. El sistema integra tres componentes inteligentes que aplican técnicas de aprendizaje automático basadas en árboles y técnicas de ingeniería del conocimiento. En los estudios realizados en el marco de este trabajo para obtener estos modelos y validarlos, se emplearon un conjunto de datos reales constituido por 26.228 registros de pacientes con diagnóstico de meningitis, procedentes de Brasil. Los resultados ponen de manifiesto que el sistema es capaz de determinar con éxito si el paciente tiene meningitis, si esta es meningocócica y si es viral o bacteriana