Agent-based modeling is an object-oriented, discrete event, population-focused method for the computational representation of dynamic systems. Agent-based models (ABMs) treat systems as aggregates of populations of interacting components governed by rules. This means of system representation allows ABMs to map well to how biological knowledge is represented and communicated. As a result, agent-based modeling is an intuitive means by which biomedical researchers can represent their knowledge in a dynamic computational form and in so doing can lower the threshold for the general biological researcher to engage in computational modeling. ABMs are particularly suited for representing the behavior of populations of cells (i.e., “cell-as-agents”) but have also been used to model lower level processes, such as molecular interactions when spatial and structural properties are involved, as well as higher level systems, such as in human populations in epidemiological studies. For purposes of its use in translational systems biology, we focus on the use of cell/tissue-as-agent ABMs and demonstrate how agent-based modeling can serve as an integrating framework for dynamic knowledge representation of biological systems.

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Agent-based models in translational systems biology

Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach.

/pdf/using-ontologies-for-simulation-modeling-7qv8988tgj.pdf

Using ontologies for simulation modeling

https://re.public.polimi.it/bitstream/11311/960940/1/Kadar_Terkaj_Sacco_CIRP_ANNALS_2013_postprint.pdf

Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems

Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.

Ontologies for transportation research: A survey

This article not only reveals the “cost price” concept, but also analyzes its essence from agricultural production position. The article tells about the methodology of identifying the relationship between milk cost price and different factors, which take place in agricultural enterprises activities. The authors operate with such concepts as “grouping” and “analysis of variance”, which are the ways of formulating and confirming their own conclusions.

Анализ себестоимости зерна и пути её снижения на примере СХА «Левашовка» аннинского района и других хозяйств аннинского и калачеевского районов Воронежской области

This paper identifies and addresses the issues associated with modeling and simulation and multiple levels of abstraction, 
or multi-resolution modeling (MRM). An extensive literature review is conducted to encompass all schools of thought in 
the area into this research. We begin by outlining the need for MRM and describe the problems encountered when two or 
more models developed at different resolutions are to be integrated into a single application. These problems can 
manifest themselves in different ways in the model, depending on the specific phenomenon being modeled. A distinction 
is made in identifying these manifestations based on whether the underlying model is a process model such as an IDEF3 
model, or an executable simulation model. Heuristic approaches have developed to assist with different aspects of model 
composability efforts. Finally, a rule-based approach has been developed to identify any such problems, or abstraction 
mismatches, that may occur if the two models are integrated into a single application. A conceptual description of these 
rules and their motivation is provided.

A framework for modeling and simulation at multiple levels of abstraction

Knowledge acquisition/discovery, ontology management, knowledge representation and knowledge sharing are key
issues in ontology research. This paper focuses on the issue of applying ontology management and analysis to facilitate
the reusability of simulation components based on ontology comparisons. Novel ontology comparison methods for
component-based simulation composition are described, such as the four independent approaches for ontology similarity
analysis: terminology-based, feature-based, semantic, and topological. A data -fusion-based approach is used to
integrate the information from these three techniques into a single similarity score. The assignment of such similarity
indices and the use of data fusion to obtain an ontology comparison similarity score are demonstrated using a simple example.

Mukul Patki

Papers

Using ontologies for simulation modeling

A framework for modeling and simulation at multiple levels of abstraction

Using ontology comparison methods for simulation composition