Building Optimal Macroscopic Representations of Complex Multi-agent Systems
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.
KeywordsLarge-scale MAS Agent aggregation Macroscopic representation Information theory Geographical and news analysis
This work was partially funded by the ANR CORPUS GEOMEDIA project (ANR-GUI-AAP-04). We would like to thank Claude Grasland, Timothée Giraud and Marta Severo for their work on this project; and Lucas M. Schnorr for his close participation to previous work.
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