By Matthias Klusch, Rainer Unland, Onn Shehory, Alexander Pokahr, Sebastian Ahrndt
This publication constitutes the complaints of the 14th German convention on Multiagent approach applied sciences, pals 2016, held in Klagenfurt, Austria, in September 2016.
12 lengthy papers and five brief papers have been conscientiously reviewed and chosen from 28 submissions.
MATES 2016 convention talks coated a large zone of themes of curiosity together with MAS engineering and modeling, problems with human-agent interplay, collaboration and coordination, agent-based edition and optimization, and purposes of MAS, specifically within the shrewdpermanent strength domain.
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Extra resources for Multiagent System Technologies: 14th German Conference, MATES 2016, Klagenfurt, Österreich, September 27-30, 2016. Proceedings
Example text
ECMDA-FA 2006. LNCS, vol. 4066, pp. 284–298. Springer, Heidelberg (2006) 14. : Agent-based simulation replication: a model driven architecture approach. , Terashima-Mar´ın, H. ) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 244–253. Springer, Heidelberg (2005) 15. : An Introduction to Multiagent Systems. Wiley, New York (2009) 16. : A methodology for creating ontology-based multiagent systems with an experiment in financial application development. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp.
48] use the same classification in their survey, further subdividing the domain of knowledge-based approaches into taxonomies and ontologies. Taxonomies are organised by the generalisation-specialisation relationship only, while ontologies are taxonomic structures enriched with other semantic relationships. Based on this classification we selected three state of the art approaches, using them as a baseline during our evaluation. We selected the Electronic Lexical Knowledge Base [19] (taxonomy-based), the Bidirectional One-Step approach [8] (ontology-based), and the Word2Vec [26] (corpus-based) approach as typical representatives of their group.
The proposed solution has been envisaged to facilitate the creation of agent-based electricity market simulations by using ontologies as the driving force of a MDD approach. We have proposed a model which merge MDD and ODCM approaches to offer a set of methods able to generate automatically agent-based models from the design of a conceptual model. We have presented and explained the phases of the model by distinguishing three main stages: (a) a conceptual model transformation; (b) an organizational agent architecture adaption; and (c) agentbased generic code generation.
