Building Optimal Macroscopic Representations of Complex Multi-agent Systems: Application to the Spatial and Temporal Analysis of International Relations through News Aggregation

Robin Lamarche-Perrin 1, 2 Yves Demazeau 2 Jean-Marc Vincent 1
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 MAGMA
LIG - Laboratoire d'Informatique de Grenoble
Abstract : The design and the debugging of large-scale MAS require abstraction tools in order to work at a macroscopic level of description. Agent aggregation provides such abstractions by reducing the complexity of the system’s microscopic representation. Since it leads to an information loss, such a key process may be extremely harmful for the analysis if poorly executed. This paper presents measures inherited from information theory to evaluate abstractions and to provide the experts with feedback regarding the quality of generated representations. Several evaluation techniques are applied to the spatial and temporal aggregation of an agent-based model of international relations. The information from on-line newspapers constitutes a complex microscopic representation of the agent states. Our approach is able to evaluate geographical abstractions used by the domain experts in order to provide efficient and meaningful macroscopic representations of the world global state.
Type de document :
Article dans une revue
LNCS Transactions on Computational Collective Intelligence, 2014
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https://hal.archives-ouvertes.fr/hal-01263846
Contributeur : Robin Lamarche-Perrin <>
Soumis le : jeudi 28 janvier 2016 - 12:19:54
Dernière modification le : samedi 30 janvier 2016 - 01:03:31

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  • HAL Id : hal-01263846, version 1

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Robin Lamarche-Perrin, Yves Demazeau, Jean-Marc Vincent. Building Optimal Macroscopic Representations of Complex Multi-agent Systems: Application to the Spatial and Temporal Analysis of International Relations through News Aggregation. LNCS Transactions on Computational Collective Intelligence, 2014. 〈hal-01263846〉

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