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Authors: Karen Figueiredo and Viviane Torres da Silva

Affiliation: Universidade Federal Fluminense (UFF), Brazil

Keyword(s): Norm, Modeling, Validation, Metamodel.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems

Abstract: Norms in multi-agent systems are mechanisms used to restrict the behavior of system entities during a period of time by defining what the entities are obligated, permitted or prohibited to do and by stating stimulus to their fulfillment by defining rewards and discouraging their violation by pointing out punishments. In this paper we propose a modeling language called NormML that makes possible the modeling of the norms together with its main properties and characteristics.

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Paper citation in several formats:
Figueiredo, K. and Torres da Silva, V. (2011). Norm-ML - A Modeling Language to Model Norms. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-8425-41-6; ISSN 2184-433X, SciTePress, pages 232-237. DOI: 10.5220/0003179502320237

@conference{icaart11,
author={Karen Figueiredo. and Viviane {Torres da Silva}.},
title={Norm-ML - A Modeling Language to Model Norms},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2011},
pages={232-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003179502320237},
isbn={978-989-8425-41-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Norm-ML - A Modeling Language to Model Norms
SN - 978-989-8425-41-6
IS - 2184-433X
AU - Figueiredo, K.
AU - Torres da Silva, V.
PY - 2011
SP - 232
EP - 237
DO - 10.5220/0003179502320237
PB - SciTePress