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Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy / Patrick Taillandier in International journal of geographical information science IJGIS, vol 25 n° 10-12 (october december 2011)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 25 n° 10-12 (october december 2011) . - pp 1971 - 1999 Titre : Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy Type de document : Article/Communication Auteurs : Patrick Taillandier , Auteur ; Cécile Duchêne , Auteur ; Alexis Drogoul, Auteur Année de publication : 2011 Article en page(s) : pp 1971 - 1999 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] AGENT [Termes IGN] bati [Termes IGN] contrôle qualité [Termes IGN] généralisation cartographique automatisée [Vedettes matières IGN] Généralisation Mots-clés libres : automated generalisation  rule revision  trial and error strategy Résumé : (auteur) Automating the generalisation process, a major issue for national mapping agencies, is extremely complex. Several works have proposed to deal with this complexity using a trial and error strategy. The performance of systems based on such a strategy is directly dependent on the quality of the control knowledge (i.e. heuristics) used to guide the trials. Unfortunately, most of the time, the definition and updation of knowledge is a fastidious task. In this context, automatic knowledge revision can not only improve the performance of the generalisation, but also allow it to automatically adapt to various usages and evolve when new elements are introduced. In this article, an offline knowledge revision approach is proposed, based on a logging of the system and on the analysis of outcoming logs. This approach is dedicated to the revision of control knowledge expressed by production rules. We have implemented and tested this approach for the automated generalisation of groups of buildings within a generalisation model called AGENT, from initial data that reference a scale of approximately 1:15,000 compared with the target map's scale of 1:50,000. The results show that our approach improves the quality of the control knowledge and thus the performance of the system. Moreover, the approach proposed is generic and can be applied to other systems based on a trial and error strategy, dedicated to generalisation or not. Numéro de notice : A2011-580 Affiliation des auteurs : IGN+Ext (1940-2011) Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2011.566568 Date de publication en ligne : 02/11/2011 En ligne : http://dx.doi.org/10.1080/13658816.2011.566568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83479 [article]

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