Ontology‐based Multi‐agent System to Support Business Users and Management / Daugiaagentine Sistema Grindžiama Ontologija Verslo Vartotojams Ir Vadybininkams Paremti

Lavbič, Dejan; Vasilecas, Olegas; Rupnik, Rok

doi:10.3846/tede.2010.21

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…When considering only large software projects, only 10 percent were successful, 52 percent were challenged and 38 percent have failed. This is at least worrying as large software projects failure may negatively affect the whole implementing enterprise (Bernroider et al, 2014;Hong and Kim, 2002;Lavbič et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing organizational risks in software projects: Stakeholder resistance

Vrhovec

Hovelja

Vavpotič

et al. 2015

International Journal of Project Management

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Section: Introductionmentioning

confidence: 99%

Diagnosing organizational risks in software projects: Stakeholder resistance

Vrhovec

Hovelja

Vavpotič

et al. 2015

International Journal of Project Management

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“…The model is a MAS with the ontology based on the standard IEC 61499 (automation system standard) [60]. The goal of this study is to optimize the energy consumption in an office building where the ontology provides the communication logic and allows agents in the model to share knowledge and data [61]. In the MAS model, agents communicate and collaborate towards a common goal.…”

Section: Mas For Demand-side and Building Systemsmentioning

confidence: 99%

The Application of Ontologies in Multi-Agent Systems in the Energy Sector: A Scoping Review

Schultz

Christensen

et al. 2019

Energies

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Multi-agent systems are well-known for their expressiveness to explore interactions and knowledge representation in complex systems. Multi-agent systems have been applied in the energy domain since the 1990s. As more applications of multi-agent systems in the energy domain for advanced functions, the interoperability raises challenge raises to an increasing requirement for data and information exchange between systems. Therefore, the application of ontology in multi-agent systems needs to be emphasized and a systematic approach for the application needs to be developed. This study aims to investigate literature on the application of ontology in multi-agent systems within the energy domain and map the key concepts underpinning these research areas. A scoping review of the existing literature on ontology for multi-agent systems in the energy domain is conducted. This paper presents an overview of the application of multi-agent systems (MAS) and ontologies in the energy domain with five aspects of the definition of agent and MAS; MAS applied in the energy domain, defined ontologies in the energy domain, MAS design methodology, and architectures, and the application of ontology in the MAS development. Furthermore, this paper provides a recommendation list for the ontology-driven multi-agent system development with the aspects of 1) ontology development process in MAS design, 2) detail design process and realization of ontology-driven MAS development, 3) open standard implementation and adoption, 4) inter-domain MAS development, and 5) agent listing approach.

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“…Also, as already stressed in [30], semantics behind the links of the two layers necessarily differ. (For incorporation of semantics see, e.g., [89].) Since assortativity coexists with transitivity [21], a relation that produces communities cannot generate, e.g., disconnected functional modules.…”

Section: Degree and Clustering Mixingmentioning

confidence: 99%

Clustering assortativity, communities and functional modules in real-world networks

Šubelj,

Bajec

2012

Preprint

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Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. Clustering, community structure and assortative mixing by degree are perhaps among most prominent examples of the latter. Although generally accepted for social networks, these properties only partially explain the structure of other networks. We first show that degree-corrected clustering is in contrast to standard definition highly assortative. Yet interesting on its own, we further note that non-social networks contain connected regions with very low clustering. Hence, the structure of real-world networks is beyond communities. We here investigate the concept of functional modules-groups of regularly equivalent nodes-and show that such structures could explain for the properties observed in non-social networks. Real-world networks might be composed of functional modules that are overlaid by communities. We support the latter by proposing a simple network model that generates scale-free small-world networks with tunable clustering and degree mixing. Model has a natural interpretation in many real-world networks, while it also gives insights into an adequate community extraction framework. We also present an algorithm for detection of arbitrary structural modules without any prior knowledge. Algorithm is shown to be superior to state-of-the-art, while application to real-world networks reveals well supported composites of different structural modules that are consistent with the underlying systems. Clear functional modules are identified in all types of networks including social. Our findings thus expose functional modules as another key ingredient of complex real-world networks.

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Ontology‐based Multi‐agent System to Support Business Users and Management / Daugiaagentine Sistema Grindžiama Ontologija Verslo Vartotojams Ir Vadybininkams Paremti

Cited by 13 publications

References 30 publications

Diagnosing organizational risks in software projects: Stakeholder resistance

Diagnosing organizational risks in software projects: Stakeholder resistance

The Application of Ontologies in Multi-Agent Systems in the Energy Sector: A Scoping Review

Clustering assortativity, communities and functional modules in real-world networks

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