MagentaToolkit: A Set of Multi-agent Tools for Developing Adaptive Real-Time Applications

Rzevski, George; Skobelev, Petr; Андреев, В. В.

doi:10.1007/978-3-540-74481-8_29

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…On this market, the indi vidual choice of each agent is implemented using a query on an ontology database containing data about the available resources and their properties with the subsequent selection from the set of offers received as a result of the query processing. More detailed information and examples of the effective use of the DR networks can be found in [9][10][11][12].…”

Section: Self Organizing Demand and Resource Networkmentioning

confidence: 99%

“…In accordance with the concept of DR networks, an applica tion MAS is build from a large number of very simple autonomous agents that can perceive information on the market, make decisions, and interact with other agents. The concept of DR networks was imple mented in a toolkit (see [9]. Using this toolkit, several large scale applications in transport logistics were developed (see [10][11][12]).…”

Section: Self Organizing Demand and Resource Networkmentioning

confidence: 99%

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Self-organization and multiagent systems: II. Applications and the development technology

Городецкий

2012

J. Comput. Syst. Sci. Int.

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Major trends in the development of modern information technologies are to a large extend determined by important practical problems that arise in economics, ecology, safety of society and individuals, and in other fields. Even though these problems seem to be quite different and the require ments for their software implementation are also different, they have many common features, which imply the most stringent requirements for modern information technologies. These features were ana lyzed in the first part of the present paper. That analysis showed that the new requirements for the model and software implementation of such problems are best met by the multiagent self organizing system model. In this paper, we consider examples of using this model in various applications and describe their architectures and software implementation; in particular, multiagent self organization models as applied for flood forecasting and planning and operational enterprise management are described. New capabilities of multiagent self organizing systems are demonstrated using a self learn ing system for detecting intrusions into computer networks as an example. Here, the problem of self configuration of an overlay network is actually solved. The capabilities of a multiagent self organizing system in large scale control in real time are demonstrated using adaptive traffic control in large cities. For the software implementation of multiagent self organizing systems, special development tools that are different from the existing ones are needed because the conventional top down development par adigm is inappropriate for self organizing architectures. The cause is that the global behavior of a mul tiagent self organizing system emerges due to local interactions; therefore, it cannot be predicted in advance. For that reason, the bottom up development model is more appropriate for such systems. In this paper, we give a brief review of the models and approaches proposed for this purpose. One of the promising approaches based on the use of the so called self organization design patterns is described in more detail. Results of using the multiagent self organization model are discussed and prospects of its practical application are estimated.

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Section: Self Organizing Demand and Resource Networkmentioning

confidence: 99%

Section: Self Organizing Demand and Resource Networkmentioning

confidence: 99%

Self-organization and multiagent systems: II. Applications and the development technology

Городецкий

2012

J. Comput. Syst. Sci. Int.

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“…The extracted high-level contexts are then stored in the context repository. Since, RDF and OWL are used to model the CONON, deductive reasoning is well supported in this regard (Semantic Reasoner, 2009;RDF, 2009). Furthermore, it is also used to check context consistency and reliability, which might be caused by imperfect monitoring.…”

Section: Context Managementmentioning

confidence: 99%

Ontology‐based context‐sensitive computing for FMS optimization

Uddin

Puttonen

Scholze

et al. 2012

Assembly Automation

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2014),"Maximizing agreement on diverse ontologies with "wisdom of crowds" relation classification", Online Information Review, Vol. 38 Iss 5 pp. 616-633 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to present an ontology-based approach of context-sensitive computing for the optimization of flexible manufacturing systems (FMS). Design/methodology/approach -A context-sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology-based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA-based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported. Findings -Continuous improvement of the factory can be enhanced utilizing context-sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.Research limitations/implications -The performance of context-sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade-off should be in between the extent of context processing and the required outcome of the support applications. Practical implications -This paper includes the practical implications of context-sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS. Originality/value -Reported results provide a modular approach of context-sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

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“…Magenta Technology provides another example of active investigation into exploration of ontologies in agent-based applications. Rzevski et al (2007) report on a set of multi-agent tools and describe details of the Ontology Management Toolset. This tool enables users to design and edit both the static and dynamic (called scenes here) aspects of an ontology.…”

Section: Domain-specific Ontologies For Agent-based Manufacturing Conmentioning

confidence: 99%

“…The ontology should provide the agent also with the explicitly defined rules (or directly a program code) to be executed by the agent to react appropriately. Magenta agent runtime environment (Rzevski et al, 2007) provides such feature -program code is sent to the agent to modify or extend its behavior. In the effort to deploy ontologies in the MAST system (Vrba, 2006), we have recently also implemented such a feature (see next section).…”

Section: Common Properties Of Ontologies Deployment In Agent-based Mamentioning

confidence: 99%

The Impacts of Semantic Technologies on Industrial Systems

Mařı́k

Vrba²,

Obitko³

2009

Handbook of Research on Social Dimensions of Semantic Technologies and Web Services

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Industrial systems face the challenges of robust and flexible control of industrial processes while satisfying the demand of mass customization and reduced time to market. To meet these requirements, systems need to work in a distributed manner, because the traditional centralized approaches are not sufficient. To achieve automated cooperation and coordination of distributed components at a larger scale, semantic technologies are necessary to enable truly open systems. The authors review state of the art of the research of ontologies, Semantic Web and Semantic Web services, together with advances of usage of semantic technologies in industry. The usage of semantic technologies is illustrated on two applications – semantics in multi-agent manufacturing systems and structural search in industrial data.

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MagentaToolkit: A Set of Multi-agent Tools for Developing Adaptive Real-Time Applications

Cited by 14 publications

References 4 publications

Self-organization and multiagent systems: II. Applications and the development technology

Self-organization and multiagent systems: II. Applications and the development technology

Ontology‐based context‐sensitive computing for FMS optimization

The Impacts of Semantic Technologies on Industrial Systems

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