@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00947933v1> a dcat:Dataset ;
    dct:description """
              The design and 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 microscopic description complexity. Since it leads to an information loss, such a key process may be extremely harmful if poorly executed. This research report presents measures inherited from information theory (Kullback-Leibler divergence and Shannon entropy) to evaluate ab- stractions and to provide the experts with feedbacks regarding the generated descriptions. Several evaluation techniques are applied to the spatial aggregation of an agent-based model of international rela- tions. The information from on-line newspapers constitutes a complex microscopic description of agent states. Our approach is able to evalu- ate geographical abstractions used by experts and to deliver them with e cient and meaningful macroscopic descriptions of the world state.
            """ ;
    dct:identifier "Report N°: RR-LIG-035" ;
    dct:issued "2026-05-06T08:46:25.717371"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-06T08:46:25.717375"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "How to Build the Best Macroscopic Description of your Multi-agent System? Application to News Analysis of International Relations" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00947933v1/resource/de5a2755-75f1-4d6d-bb06-ae64382dabc1> ;
    dcat:keyword "agent-aggregation",
        "geographical-and-news-analysis",
        "infoeu-reposemanticsreport",
        "infoinfo-macomputer-science-csmultiagent-systems-csma",
        "information-theory",
        "large-scale-multi-agent-systems",
        "macroscopic-description",
        "reports" ;
    dcat:landingPage <https://inria.hal.science/hal-00947933> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00947933v1/resource/de5a2755-75f1-4d6d-bb06-ae64382dabc1> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-06T08:46:25.728445"^^xsd:dateTime ;
    dct:modified "2026-05-06T08:46:25.694403"^^xsd:dateTime ;
    dct:title "How to Build the Best Macroscopic Description of your Multi-agent System? Application to News Analysis of International Relations" ;
    dcat:accessURL <https://inria.hal.science/hal-00947933> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://inria.hal.science/hal-00947933> a foaf:Document .