Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552057
Lin Zhang, Fei Ye, Y. Laili, Kunyu Xie, Pengfei Gu, Xiaohan Wang, Chun Zhao, Xuesong Zhang, Minjie Chen
Modeling and simulation have become an important means of supporting analyses and development of complex products. At present, for the development of full-process and full-system modeling and simulation, system modeling languages (such as SysML) are often required to cooperate with multi-physics modeling languages and simulation platforms (such as Modelica, Simulink), which is difficult to ensure the true unity of the whole system, the consistency between the various layers and the traceability of the modeling and simulation process. In response to this problem, this paper proposes a new integrated intelligent modeling and simulation language—X language, which supports the description of system-layer structure and physical behavior, as well as modeling of complex agent models. Interpreter and engine are developed to enable X language to support the simulation of continuous, discrete event and agent models. Finally, the tank model is taken as a case to verify the modeling and simulation capabilities of the X language.
{"title":"X Language: An Integrated Intelligent Modeling and Simulation Language for Complex Products","authors":"Lin Zhang, Fei Ye, Y. Laili, Kunyu Xie, Pengfei Gu, Xiaohan Wang, Chun Zhao, Xuesong Zhang, Minjie Chen","doi":"10.23919/ANNSIM52504.2021.9552057","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552057","url":null,"abstract":"Modeling and simulation have become an important means of supporting analyses and development of complex products. At present, for the development of full-process and full-system modeling and simulation, system modeling languages (such as SysML) are often required to cooperate with multi-physics modeling languages and simulation platforms (such as Modelica, Simulink), which is difficult to ensure the true unity of the whole system, the consistency between the various layers and the traceability of the modeling and simulation process. In response to this problem, this paper proposes a new integrated intelligent modeling and simulation language—X language, which supports the description of system-layer structure and physical behavior, as well as modeling of complex agent models. Interpreter and engine are developed to enable X language to support the simulation of continuous, discrete event and agent models. Finally, the tank model is taken as a case to verify the modeling and simulation capabilities of the X language.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"70 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89622609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552167
E. Navarro-López, N. Çabukoglu
A framework for the multi-phase epidemic modelling of SEIARD (Susceptible-Exposed-symptomatic Infectious-Asymptomatic infectious-Recovered by immunity or by vaccination-Dead due to the disease) subpopulations is produced with switching transmission rate, basic reproduction ratio and vaccination strategy. The key novel feature of our model is that we reproduce the different phases of the evolution of the infectious disease by using a hybrid automaton with different discrete locations corresponding to each of the phases of the disease. This is a general modelling framework applicable to the spreading of infectious diseases. We show how the proposed model works with the simulation of different scenarios.
{"title":"A Novel Hybrid Automaton Framework for Multi-Phase Epidemic Modelling","authors":"E. Navarro-López, N. Çabukoglu","doi":"10.23919/ANNSIM52504.2021.9552167","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552167","url":null,"abstract":"A framework for the multi-phase epidemic modelling of SEIARD (Susceptible-Exposed-symptomatic Infectious-Asymptomatic infectious-Recovered by immunity or by vaccination-Dead due to the disease) subpopulations is produced with switching transmission rate, basic reproduction ratio and vaccination strategy. The key novel feature of our model is that we reproduce the different phases of the evolution of the infectious disease by using a hybrid automaton with different discrete locations corresponding to each of the phases of the disease. This is a general modelling framework applicable to the spreading of infectious diseases. We show how the proposed model works with the simulation of different scenarios.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"79 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88141632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552089
P. Giabbanelli, J. Badham, B. Castellani, H. Kavak, V. Mago, A. Negahban, S. Swarup
The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to guide decision makers. However, a year into the COVID-19 pandemic, the computational science literature lacks a proper internal exploration of the modeling journey of researchers around the world, including how they responded to the shared challenges our community faced such as data limitations, model fitting and working with public stakeholders. The current paper is a detailed examination of the internal processes of six research teams, which were funded in several countries to model COVID-19. Each team was asked to reflect on the research question and how they solved their respective modeling challenges, as well as how, looking back, they would do things differently.
{"title":"Opportunities and Challenges in Developing COVID-19 Simulation Models: Lessons from Six Funded Projects","authors":"P. Giabbanelli, J. Badham, B. Castellani, H. Kavak, V. Mago, A. Negahban, S. Swarup","doi":"10.23919/ANNSIM52504.2021.9552089","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552089","url":null,"abstract":"The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to guide decision makers. However, a year into the COVID-19 pandemic, the computational science literature lacks a proper internal exploration of the modeling journey of researchers around the world, including how they responded to the shared challenges our community faced such as data limitations, model fitting and working with public stakeholders. The current paper is a detailed examination of the internal processes of six research teams, which were funded in several countries to model COVID-19. Each team was asked to reflect on the research question and how they solved their respective modeling challenges, as well as how, looking back, they would do things differently.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"9 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88386741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552132
J. Fryer, Paulo Garcia
Extant pandemic detection and prevention strategies are based around contact tracing technologies. In contrast, this manuscript describes a strategy for preemptive pandemic response based on wearable devices with built-in privacy, based on distributed, encrypted and anonymized contact chains. We evaluate such strategy in an agent-based simulation environment that models a wearable-device equipped population in an urban area, comparing it with no response strategy, and with the extant contact-tracing approaches. Our results suggest contact-chaining is an effective way to augment pandemic response, by preemptively isolating persons who are potentially infectious; initial results indicate up to a 23.4% reduction in peak infections compared to the strictest extant approach. Simulations show that this strategy is especially effective during a first wave and can potentially prevent further infection waves. Ongoing work is looking at the privacy issues of such an approach and modeling countermeasures within our simulation framework.
{"title":"Modeling Pandemic Response for Populations Equipped with Contact-Chain Capable Wearable Devices","authors":"J. Fryer, Paulo Garcia","doi":"10.23919/ANNSIM52504.2021.9552132","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552132","url":null,"abstract":"Extant pandemic detection and prevention strategies are based around contact tracing technologies. In contrast, this manuscript describes a strategy for preemptive pandemic response based on wearable devices with built-in privacy, based on distributed, encrypted and anonymized contact chains. We evaluate such strategy in an agent-based simulation environment that models a wearable-device equipped population in an urban area, comparing it with no response strategy, and with the extant contact-tracing approaches. Our results suggest contact-chaining is an effective way to augment pandemic response, by preemptively isolating persons who are potentially infectious; initial results indicate up to a 23.4% reduction in peak infections compared to the strictest extant approach. Simulations show that this strategy is especially effective during a first wave and can potentially prevent further infection waves. Ongoing work is looking at the privacy issues of such an approach and modeling countermeasures within our simulation framework.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"67 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90377778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552073
Simon Thrane Hansen, C. Gomes, P. Larsen, J. Pol
Simulation-based analyses of cyber-physical systems are becoming increasingly vital. Co-simulation is one such technique that enables the coupling of specialized simulation tools through an orchestration algorithm that dictates when, with which inputs, and how far each simulation tool should advance its corresponding subsystem. The result of a co-simulation is often sensitive to the orchestration algorithm. This algorithm should be designed based on the simulation tool's implementation to minimize the co-simulation error. The paper extends current graph-based approaches for generating implementation-aware simulation algorithms to cover complex co-simulation scenarios where step negotiation and algebraic loops are part of the simulation. We show how the generation takes place for different complex co-simulation scenarios. An implementation of the approach is available online.
{"title":"Synthesizing Co-Simulation Algorithms with Step Negotiation and Algebraic Loop Handling","authors":"Simon Thrane Hansen, C. Gomes, P. Larsen, J. Pol","doi":"10.23919/ANNSIM52504.2021.9552073","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552073","url":null,"abstract":"Simulation-based analyses of cyber-physical systems are becoming increasingly vital. Co-simulation is one such technique that enables the coupling of specialized simulation tools through an orchestration algorithm that dictates when, with which inputs, and how far each simulation tool should advance its corresponding subsystem. The result of a co-simulation is often sensitive to the orchestration algorithm. This algorithm should be designed based on the simulation tool's implementation to minimize the co-simulation error. The paper extends current graph-based approaches for generating implementation-aware simulation algorithms to cover complex co-simulation scenarios where step negotiation and algebraic loops are part of the simulation. We show how the generation takes place for different complex co-simulation scenarios. An implementation of the approach is available online.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"78 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78958720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552105
J. Possik, C. Yaacoub, S. Gorecki, G. Zacharewicz, A. D’Ambrogio
MBSE (Model Based Systems Engineering) is considered a valuable and effective approach not only in engineering domains, although it appears to bring a potential assistance to artistic visions and initiatives. The cross-domain capitalization is the key to further innovation and advances in technology. This paper describes an MBSE approach, applied to the development of a complex multi-component arrangement system, to generate an automated music arrangement for any melody, with a harmonically correct result. The user introduces the melody in the form of a MIDI file. The system analyzes the melody, detects the scale, then follows algorithms based on thorough theoretical and harmonic studies, to finally generate the arrangement based on the selected music genre (classical, jazz, pop, etc.). Compared to other existing systems in the market, the developed system largely reduces the dissonance between the melody and the arrangement, which makes the final arrangement a good accompaniment to the melody.
{"title":"A Model Based Systems Engineering Approach to Automated Music Arrangement","authors":"J. Possik, C. Yaacoub, S. Gorecki, G. Zacharewicz, A. D’Ambrogio","doi":"10.23919/ANNSIM52504.2021.9552105","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552105","url":null,"abstract":"MBSE (Model Based Systems Engineering) is considered a valuable and effective approach not only in engineering domains, although it appears to bring a potential assistance to artistic visions and initiatives. The cross-domain capitalization is the key to further innovation and advances in technology. This paper describes an MBSE approach, applied to the development of a complex multi-component arrangement system, to generate an automated music arrangement for any melody, with a harmonically correct result. The user introduces the melody in the form of a MIDI file. The system analyzes the melody, detects the scale, then follows algorithms based on thorough theoretical and harmonic studies, to finally generate the arrangement based on the selected music genre (classical, jazz, pop, etc.). Compared to other existing systems in the market, the developed system largely reduces the dissonance between the melody and the arrangement, which makes the final arrangement a good accompaniment to the melody.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"1 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78996402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552173
Y. Ramesh, M. Rao
Statistical Model Checking (SMC) for the analysis of Multi-Agent Systems has been studied in the recent past. A feature peculiar to Multi-Agent Systems in the context of Statistical Model Checking is that of aggregate queries–temporal logic formula that involve a large number of agents. To answer such queries through Monte Carlo sampling, the statistical approach to model checking simulates the entire agent population and evaluates the query. This makes the simulation overhead significantly higher than the query evaluation overhead. To alleviate this problem, one strategy is to choose only a subset of the agents to simulate, through sampling. Further, this problem becomes particularly challenging when the model checking queries involve multiple attributes of the agents. We propose a population sampling algorithm that simulates only a subset of all the agents and scales to multiple attributes, thus making the solution generic. The population sampling approach results in increased efficiency (a gain in running time of 50% to 100% in most experiments) for a marginal loss in accuracy (between 1% to 5% in most experiments), especially for queries that involve limited time horizons.
{"title":"Multi-Attribute Queries for Stochastic Multi Agent Systems over Short Time Horizons","authors":"Y. Ramesh, M. Rao","doi":"10.23919/ANNSIM52504.2021.9552173","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552173","url":null,"abstract":"Statistical Model Checking (SMC) for the analysis of Multi-Agent Systems has been studied in the recent past. A feature peculiar to Multi-Agent Systems in the context of Statistical Model Checking is that of aggregate queries–temporal logic formula that involve a large number of agents. To answer such queries through Monte Carlo sampling, the statistical approach to model checking simulates the entire agent population and evaluates the query. This makes the simulation overhead significantly higher than the query evaluation overhead. To alleviate this problem, one strategy is to choose only a subset of the agents to simulate, through sampling. Further, this problem becomes particularly challenging when the model checking queries involve multiple attributes of the agents. We propose a population sampling algorithm that simulates only a subset of all the agents and scales to multiple attributes, thus making the solution generic. The population sampling approach results in increased efficiency (a gain in running time of 50% to 100% in most experiments) for a marginal loss in accuracy (between 1% to 5% in most experiments), especially for queries that involve limited time horizons.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"94 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77260330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552123
Bruno St-Aubin, Jon Menard, Gabriel A. Wainer
The Discrete Event System Specification (DEVS) is a hierarchical and modular simulation formalism that can represent an unlimited range of real-world systems. Its flexibility comes at a cost: increased complexity when developing simulation models, less model reusability, and lowered adoption rates. In addition, the DEVS software environment is generally fragmented and siloed. Tools have been developed to support model debugging or simulation trace visualization but, they are often closely coupled to simulators and consequently their usefulness is limited. We present a web-based modeling and simulation environment that is meant to alleviate some of the pressure points faced by DEVS users across the simulation lifecycle. The environment provides a library of reusable models meant to favor sharing models and collaboration, a workflow-based approach for modeling large scale systems, front-end resources in the form of an API to develop case-specific simulation-based applications and a set of web resources to facilitate the simulation lifecycle management.
{"title":"A Web Based Modeling and Simulation Environment to Support the DEVS Simulation Lifecycle","authors":"Bruno St-Aubin, Jon Menard, Gabriel A. Wainer","doi":"10.23919/ANNSIM52504.2021.9552123","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552123","url":null,"abstract":"The Discrete Event System Specification (DEVS) is a hierarchical and modular simulation formalism that can represent an unlimited range of real-world systems. Its flexibility comes at a cost: increased complexity when developing simulation models, less model reusability, and lowered adoption rates. In addition, the DEVS software environment is generally fragmented and siloed. Tools have been developed to support model debugging or simulation trace visualization but, they are often closely coupled to simulators and consequently their usefulness is limited. We present a web-based modeling and simulation environment that is meant to alleviate some of the pressure points faced by DEVS users across the simulation lifecycle. The environment provides a library of reusable models meant to favor sharing models and collaboration, a workflow-based approach for modeling large scale systems, front-end resources in the form of an API to develop case-specific simulation-based applications and a set of web resources to facilitate the simulation lifecycle management.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"8 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82691361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552110
S. Lee, J. Rozenblit, K. Gothard
We are interested in identifying the learning status of the social behavioral tasks in the rhesus monkey. In addition, we define the characteristic of stimulus with a numerical quantification. We allow monkeys to interact with individuals of different social status, while we monitor the viewer monkey's behavior by tracking its scan paths. With these observations, we can understand the learning status of this animal via looking behavior analysis on the stimulus. First, the viewer monkey shows different looking patterns among six different classes. Therefore, we can generate different data descriptors of these classes and observe the classification performance of the machine learning algorithm. Second, we design the ground truth model based on the characteristic of each stimulus. We define the distribution of information from the ratio of the face, body, and background area in the stimulus. Lastly, we link them to figure out whether the viewer monkey learned enough about the information in the stimulus.
{"title":"Decision of Learning Status Based on Modeling of the Information Measurement of Social Behavioral Tasks in Rhesus Monkeys","authors":"S. Lee, J. Rozenblit, K. Gothard","doi":"10.23919/ANNSIM52504.2021.9552110","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552110","url":null,"abstract":"We are interested in identifying the learning status of the social behavioral tasks in the rhesus monkey. In addition, we define the characteristic of stimulus with a numerical quantification. We allow monkeys to interact with individuals of different social status, while we monitor the viewer monkey's behavior by tracking its scan paths. With these observations, we can understand the learning status of this animal via looking behavior analysis on the stimulus. First, the viewer monkey shows different looking patterns among six different classes. Therefore, we can generate different data descriptors of these classes and observe the classification performance of the machine learning algorithm. Second, we design the ground truth model based on the characteristic of each stimulus. We define the distribution of information from the ratio of the face, body, and background area in the stimulus. Lastly, we link them to figure out whether the viewer monkey learned enough about the information in the stimulus.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"265 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73106813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552162
M. Blas, S. Gonnet, B. Zeigler
The Discrete Event System Specification (DEVS) is one of the key formalisms to define Discrete Event Simulation (DES) models and underlying concepts. In this paper, we propose a conceptualization of DEVS designed as a metamodel that supports the formalization task. Such a conceptualization is part of a multi-level layered structure that defines a universal representation of DEVS as a combination of both theory and practice points of view. For the metamodel specification, we employ UML. With the existing modeling technology, our metamodel can be implemented to provide a framework that supports: (a) consistency validation between discrete event system descriptions and formal models using a DES metamodel, (b) interoperability between formalization and most used implementations using a platform-independent metamodel, and (c) consistency verification between formal models and their implementation. All these benefits are derived from the capability of defining model-to-model transformations over the modeling levels proposed in our conceptualization.
{"title":"Towards a Universal Representation of DEVS: A Metamodel-Based Definition of DEVS Formal Specification","authors":"M. Blas, S. Gonnet, B. Zeigler","doi":"10.23919/ANNSIM52504.2021.9552162","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552162","url":null,"abstract":"The Discrete Event System Specification (DEVS) is one of the key formalisms to define Discrete Event Simulation (DES) models and underlying concepts. In this paper, we propose a conceptualization of DEVS designed as a metamodel that supports the formalization task. Such a conceptualization is part of a multi-level layered structure that defines a universal representation of DEVS as a combination of both theory and practice points of view. For the metamodel specification, we employ UML. With the existing modeling technology, our metamodel can be implemented to provide a framework that supports: (a) consistency validation between discrete event system descriptions and formal models using a DES metamodel, (b) interoperability between formalization and most used implementations using a platform-independent metamodel, and (c) consistency verification between formal models and their implementation. All these benefits are derived from the capability of defining model-to-model transformations over the modeling levels proposed in our conceptualization.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"5 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73233782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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