IRM4MLS: The Influence Reaction Model for Multi-Level Simulation

Morvan, Gildąs; Veremme, Alexandre; Dupont, Daniel

doi:10.1007/978-3-642-18345-4_2

Cited by 20 publications

(27 citation statements)

References 15 publications

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“…The design phase is still less detailed compared to various current challenges of scalability. [13] has proposed the Influence Reaction Model for Multi-level Simulation (IRM4MLS) meta-model. It is a generic meta-model that aims to specify and execute MLABM.…”

Section: Multi-level Agent-based Modeling Approachesmentioning

confidence: 99%

Natural Model based Design in Context: an Effective Method for Environmental Problems

Kameni¹,

Weide²,

Groot³

2017

J. Complex Syst. Inform. Model. Q.

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Abstract. Analyzing complex problem domains is not easy. Simulation tools support decision makers to find the best policies. Model-based system development is an approach where a model of the application domain is the central driving force when designing simulation tools. State-of-the-art techniques however still require both expert knowledge of the application domain and the implementation techniques as provided by ICT (such as multilevel agent technology).Domain experts, however, usually do not master ICT sufficiently. Modeling is more insightful for the domain expert when its goal is to formalize the language being used in that domain as a semi-natural language. At the meta level, this language describes the main concepts of the type of application domain. The model then is a concretization of this meta model. The main focus of this article is (1) to propose a natural-language-based approach to modeling application domains, (2) to show how these models can be transformed systematically into computational models, and (3) to propose the tool TiC (Tool in Context) that supports the domain expert when developing a model and generating a simulation tool. Our research methodology is based on Design Science. We verify our approach by describing the various transformation steps in detail, and by demonstrating the way of working via a sample session applying a real problem of Laf Forest Reserve deforestation in North Cameroon.

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Section: Multi-level Agent-based Modeling Approachesmentioning

confidence: 99%

Natural Model based Design in Context: an Effective Method for Environmental Problems

Kameni¹,

Weide²,

Groot³

2017

J. Complex Syst. Inform. Model. Q.

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“…Their formalism is based on the multi-level agent-based model IRM4MLS, which allows the representation of multiple entities that can interoperate at different levels, i.e., a constituent can be itself an SoS, hence supporting different levels of granularity [72]. To evaluate their approach, they implemented a co-simulation of a directed SoS coping with a reconfiguration problem in the domain of intelligent autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

Stimuli-SoS: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures

et al. 2017

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Background: Systems-of-systems (SoS) are alliances of independent and interoperable software-intensive systems. SoS often support critical domains, being required to exhibit a reliable operation, specially because people's safety relies on their services. In this direction, simulations enable the validation of different operational scenarios in a controlled environment, allowing a benchmarking of its response as well as revealing possible breaches that could lead to failures. However, simulations are traditionally manual, demanding a high level of human intervention, being costly and error-prone. A stimuli generator could aid in by continuously providing data to trigger a SoS simulation and maintaining its operation. Methods: We established a model-based approach termed Stimuli-SoS to support the creation of stimuli generators to be used in SoS simulations. Stimuli-SoS uses software architecture descriptions for automating the creation of such generators. Specifically, this approach transforms SoSADL, a formal architectural description language for SoS, into dynamic models expressed in DEVS, a simulation formalism. We carried out a case study in which Stimuli-SoS was used to automatically produce stimuli generators for a simulation of a flood monitoring SoS. Results:We run simulations of a SoS architectural configuration with 69 constituent systems, i.e., 42 sensors, 9 crowdsourcing systems, and 18 drones. Stimuli generators were automatically generated for each type of constituent. These stimuli generators were capable of receiving the input data from the database and generating the expected stimuli for the constituents, allowing to simulate constituent systems interoperations into the flood monitoring SoS. Using Stimuli-SoS, we simulated 38 days of flood monitoring in little more than 6 h. Stimuli generators correctly forwarded data to the simulation, which was able to reproduce 29 flood alerts triggered by the SoS during a flooding event. In particular, Stimuli-SoS is almost 65 times more productive than a manual approach to producing data for the same type of simulation. Conclusions: Our approach succeeded in automatically deriving a functional stimuli generator that can reproduce environmental conditions for simulating a SoS. In particular, we presented new contributions regarding productivity and automation for the use of a model-based approach in SoS engineering.

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“…Managing multiple viewpoints on the same phenomenon induces the use of heterogeneous time models, thus raising issues related to time and consistency. However, simulation bias can be controlled by rules that constraint perceptions, influence production and reaction computation, according to causality and coherence principles (Morvan et al, 2011). To deal with this issue, we developed a generic approach called SIMILAR to design simulations (see Morvan and Kubera (2014) for more detailed information about the main principles of SIMILAR).…”

Section: Proposed Solutionsmentioning

confidence: 99%

A First Step Towards Dynamic Hybrid Traffic Modeling

Bouha¹,

Morvan²,

Abouaïssa³

et al. 2015

ECMS 2015 Proceedings Edited By: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova

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Hybrid traffic modeling and simulation provide an important way to represent and evaluate large-scale traffic networks at different levels of details. The first level, called "microscopic" allows the description of individual vehicles and their interactions as well as the study of driver's individual behavior. The second, based on the analogy with fluidic dynamic, is the "macroscopic" one and provides an efficient way to represent traffic flow behavior in large traffic infrastructures, using three aggregated variables: traffic density, mean speed and traffic volume. An intermediate level called "mesoscopic" considers a group of vehicles sharing common properties such as a same origin and destination. The work conducted in this paper presents a first step allowing simulation of wide area traffic network on the basis of dynamic hybrid modeling, where the representation associated to a network section can change at runtime. The proposed approach is implemented in a simulation platform, called JAM-FREE.

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IRM4MLS: The Influence Reaction Model for Multi-Level Simulation

Cited by 20 publications

References 15 publications

Natural Model based Design in Context: an Effective Method for Environmental Problems

Natural Model based Design in Context: an Effective Method for Environmental Problems

Stimuli-SoS: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures

A First Step Towards Dynamic Hybrid Traffic Modeling

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