Ontology-based Open Multi-agent Systems for Adaptive Resource Management

Petr Skobelev, Alexey Zhilyaev, Vladimir Larukhin, Sergey Grachev, Elena Simonova

Abstract

The paper describes an ontological model of a planning object, which provides flexible configuration of multi-agent resource management systems. The authors propose using the basic ontology of resource planning and then building it up for significantly different domains. The key concept here is “Task”. A relatively universal agent can be created thanks to formalized description of various classes of tasks based on this concept. It can also be customized to a specific domain area. Based on the ontology, an enterprise knowledge base is created. It contains instances of concepts and relations. The paper also introduces new classes of agents for demand and resource networks. The authors then propose a new method of multi-agent planning using this knowledge base. This approach has been already successfully applied in several domain areas through the developed software package. The paper demonstrates that the use of ontologies can improve the quality and efficiency of planning by taking into account multiple factors in real time, thus reducing the cost of creating and maintaining multi-agent systems, as well as development times and risks.

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Paper Citation


in Harvard Style

Skobelev P., Zhilyaev A., Larukhin V., Grachev S. and Simonova E. (2020). Ontology-based Open Multi-agent Systems for Adaptive Resource Management.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-395-7, pages 127-135. DOI: 10.5220/0008896301270135


in Bibtex Style

@conference{icaart20,
author={Petr Skobelev and Alexey Zhilyaev and Vladimir Larukhin and Sergey Grachev and Elena Simonova},
title={Ontology-based Open Multi-agent Systems for Adaptive Resource Management},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2020},
pages={127-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008896301270135},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Ontology-based Open Multi-agent Systems for Adaptive Resource Management
SN - 978-989-758-395-7
AU - Skobelev P.
AU - Zhilyaev A.
AU - Larukhin V.
AU - Grachev S.
AU - Simonova E.
PY - 2020
SP - 127
EP - 135
DO - 10.5220/0008896301270135