A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based Simulation, Valencia, Spain, 5th June 201

Methodological Guidelines for Engineering Self-organization and Emergence

The ASCENS project deals with the design and development of complex self-adaptive systems, where self-organization is one of the possible means by which to achieve self-adaptation. However, to support the development of self-organising systems, one has to extensively re-situate their engineering from a software architectures and requirements point of view. In particular, in this chapter, we highlight the importance of the decomposition in components to go from the problem to the engineered solution. This leads us to explain and rationalise the following architectural strategy: designing by following the problem organisation. We discuss architectural advantages for development and documentation, and its coherence with existing methodological approaches to self-organisation, and we illustrate the approach with an example on the area of swarm robotics

Evidence in peroneal nerve entrapment: A scoping review

peer reviewedBackground and purpose: Daily management of patients with foot drop due to peroneal nerve entrapment varies between a purely conservative treatment and early surgery, with no high-quality evidence to guide current practice. Electrodiagnostic (EDX) prognostic features and the value of imaging in establishing and supplementing the diagnosis have not been clearly established. Methods: We performed a literature search in the online databases MEDLINE, Embase, and the Cochrane Library. Of the 42 unique articles meeting the eligibility criteria, 10 discussed diagnostic performance of imaging, 11 reported EDX limits for abnormal values and/or the value of EDX in prognostication, and 26 focused on treatment outcome. Results: Studies report high sensitivity and specificity of both ultrasound (varying respectively from 47.1% to 91% and from 53% to 100%) and magnetic resonance imaging (MRI; varying respectively from 31% to 100% and from 73% to 100%). One comparative trial favoured ultrasound over MRI. Variable criteria for a conduction block (>20%–≥50) were reported. A motor conduction block and any baseline compound motor action potential response were identified as predictors of good outcome. Based predominantly on case series, the percentage of patients with good outcome ranged 0%–100% after conservative treatment and 40%−100% after neurolysis. No study compared both treatments. Conclusions: Ultrasound and MRI have good accuracy, and introducing imaging in the standard diagnostic workup should be considered. Further research should focus on the role of EDX in prognostication. No recommendation on the optimal treatment strategy of peroneal nerve entrapment can be made, warranting future randomized controlled trials. © 2021 European Academy of Neurolog

Model Transformation for Model Driven Development of Semantic Web Enabled Multi-Agent Systems

Model Driven Development (MDD) provides an infrastructure that simplifies Multi-agent System (MAS) development by increasing the abstraction level. In addition to defining models, transformation process for those models is also crucial in MDD. On the other hand, MAS modeling should also take care of emerging requirements of MAS deployment on the Semantic Web environment. Hence, in this paper we propose a model transformation process for MDD of Semantic Web enabled MASs. We first define source and target models for the transformation regarding the modeling of interactions between agents and semantic web services and then grant mappings between these source and model entities to derive transformation rules and constraints. Finally we realize the whole transformation for a real MAS framework by using a well-known model transformation language named ATL

Metamodeling of Semantic Web Enabled Multiagent Systems

Several agent researchers are currently studying agent modeling and they propose dierent architectural metamodels for developing Multiagent Systems (MAS) according to specic agent development methodologies. When support for Semantic Web technology and its related constructs are considered, agent metamodels should include metaentities to model MASs which work in semantic web environment. In this paper, we introduce an agent metamodel to define the required constructs of a Semantic Web enabled MAS in order to provide semantic capability modeling and interaction of agents both with other agents and semantic web services. We first give a conceptual MAS architecture to identify new constructs in addition to constructs of a traditional MAS and then we propose a metamodel including the first-class entities required by such a conceptual architecture