Conceptual Modeling for Composition of Model-Based Complex Systems

Turnitsa, Charles

doi:10.1201/9781439810385-c14

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…This difference is better observed in the example provided by Tolk et al (2010): ³An Automatic Teller Machine (ATM) is representative of a real teller as it can perform many similar interactions including necessary inputs and outputs. In M&S systems, interactions in terms of inputs and Turnitsa, Padilla and Tolk outputs are not sufficient to identify a referent because many referents have similar inputs and outputs when abstracted, which makes it impossible to identify which one the conceptualization is referring to.…”

Section: The Research Questionmentioning

confidence: 89%

Ontology for Modeling and Simulation

Turnitsa

Padilla

Tolk

2010

Proceedings of the 2010 Winter Simulation Conference

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This paper establishes what makes an ontology different in Modeling and Simulation (M&S) from other disciplines, vis-a-vis, the necessity to capture a conceptual model of a system in an explicit, unambiguous, and machine readable form. Unlike other disciplines where ontologies are used, such as Information Systems and Medicine, ontologies in M&S do not depart from a set of requirements but from a research question which is contingent on a modeler. Thus, the semiotic triangle is used to present that different implemented ontologies are representations of different conceptual models whose commonality depends on which research question is being asked. Ontologies can be applied to better capture the modeler ¶V perspective. The elicitation of ontological, epistemological, and teleological considerations is suggested. These considerations may lead to better differentiation between conceptualizations, which for a computer are of importance for use, reuse and composability of models and interoperability of simulations. INTRODUCTIONTraditionally, an ontological representation, also known as an ontology, is a specification of a system in reality. One of the most commonly referenced definitions for what an ontological representation is, with regards to information systems (vs. philosophy) is "formal explicit specification of a shared conceptualization" (Gruber, 1993). While a conceptualization, in this definition, is behind nearly any artifact (whether it be a computer program, a model, or just a diagram), the specification can vary widely with the intended use it will serve. In addition, the idea of a shared conceptualization assumes a common frame of reference, or lens, which can vary depending on the modeler. The uses of an ontology have been shown to fall into a framework (Uschold and Jasper 1999) with four categories. These categories have been shown to apply to M&S usage as well (Turnitsa and Tolk 2006). These four framework categories are:x Ontology-based search -This is useful for discovery and selection of information or components.x Neutral authoring -Especially helpful in cases of data exchange among systems.x Ontology as specification -Useful for describing the meaning of a domain, to guide system development.x Common access to information -A much stronger case than Neutral Authoring, this allows meaning to be transmitted, not just matched against.Ontologies in the first and second category are intended to assist automata in performing tasks of search and selection and tasks of meaningful information exchange respectively. Ontologies in the third 643 978-1-4244-9865-9/10/$26.00 ©2010 IEEE

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Section: The Research Questionmentioning

confidence: 89%

Ontology for Modeling and Simulation

Turnitsa

Padilla

Tolk

2010

Proceedings of the 2010 Winter Simulation Conference

Self Cite

View full text Add to dashboard Cite

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“…Functional elements in a complex model that is aimed for reuse should be loosely coupled to one another 3 [41,44]. Although components should support composition [45], it is acknowledged by many authors that model composability is difficult to apply [46][47][48]. Composability requires the adoption of principles of independence and controlled explicit dependencies [49].…”

Section: Modeling Conceptmentioning

confidence: 99%

“…Although components should support composition, 44 it is acknowledged by many authors that model composability is difficult to apply. 45–47 Composability requires the adoption of principles of independence and controlled explicit dependencies. 48 In this regard, the DEVS formalism 2 provides strong support for composition with coupling constraints.…”

Section: Modeling Conceptmentioning

confidence: 99%

Component-based light-rail modeling in discrete event systems specification (DEVS)

2015

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Rail modeling and simulation is an effective decision support instrument to plan and design complex rail infrastructures and operations. To successfully support these activities on a large scale, the simulation model should be detailed enough and yet be computationally efficient. This poses a set of challenges pertaining to the design of the models. In this paper, we propose a component based light-rail modeling and simulation library in the DEVS formalism. The proposed library is described in detail and is shown to be efficient and scalable. We conclude the paper by offering a set of good design principles derived from this experience, which are also relevant to other large scale infrastructure system simulation model design.

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“…Many authors have demonstrated the application of ontologies for the Modeling and Simulation field -M and S, which allows the definition of a conceptual model of explicitly and unambiguously and can be processed by machines (Tolk et al, 2010), (Lee and Zeigler, 2010) and (Ören, 2012). Some of these systems using an ontological approach to the modeling of industrial plants and process simulation are briefly described below.…”

Section: Related Workmentioning

confidence: 99%

Ontology based Modelling of Operator Training Simulator Scenarios from Human Error Reports

Filho

Aguiar

Vieira

2015

Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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In industrial systems' simulated environments the assimilation of technical procedures by the operators under training is enhanced by reproducing similar to real situations experienced in the workplace. The experience and learning acquired in simulators is directly related to the quality and realism of the proposed training scenarios. On the other hand, the experience acquired is even more conducive to good working practices when it involves situations known to lead into errors. Often training scenarios are dependent on the tutor's experience and the knowledge of operator difficulties in the work environment. This paper proposes a systematic approach for building training scenarios to be simulated, based on the analysis and reproduction of situations described in the working environment error reports. This approach is based on the instantiation of ontologies built for both domains, covering the knowledge on both the error situations and operator training scenarios. This study is focused in the domain of electric power systems operation.

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Conceptual Modeling for Composition of Model-Based Complex Systems

Cited by 20 publications

References 13 publications

Ontology for Modeling and Simulation

Ontology for Modeling and Simulation

Component-based light-rail modeling in discrete event systems specification (DEVS)

Ontology based Modelling of Operator Training Simulator Scenarios from Human Error Reports

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