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Auteur Alexis Drogoul |
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Affiner la recherche Interroger des sources externesGen*: a generic toolkit to generate spatially explicit synthetic populations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
Titre : Gen*: a generic toolkit to generate spatially explicit synthetic populations Type de document : Article/Communication Auteurs : Kevin Chapuis, Auteur ; Patrick Taillandier Année de publication : 2018 Article en page(s) : pp 1194 - 1210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] distribution spatiale
[Termes IGN] figuration de la densité
[Termes IGN] modèle orienté agent
[Termes IGN] population urbaine
[Termes IGN] programmation par contraintes
[Termes IGN] recensement démographique
[Termes IGN] régression
[Termes IGN] Rouen
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Agent-based models tend to integrate more and more data that can deeply impact their outcomes. Among these data, the ones that deal with agent attributes and localization are particularly important, but are very difficult to collect. In order to tackle this issue, we propose a complete generic toolkit called Gen* dedicated to generating spatially explicit synthetic populations from global (census and GIS) data. This article focuses on the localization methods provided by Gen* that are based on regression, geometrical constraints and spatial distributions. The toolkit is applied for a case study concerning the generation of the population of Rouen (France) and shows the capabilities of Gen* regarding population spatialization. Numéro de notice : A2018-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1440563 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1440563 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89875
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 1194 - 1210[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve 3L Disponible
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 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] GénéralisationMots-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
in International journal of geographical information science IJGIS > vol 25 n° 10-12 (october december 2011) . - pp 1971 - 1999[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2011061 RAB Revue Centre de documentation En réserve 3L Disponible
Titre : Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation Type de document : Article/Communication Auteurs : Patrick Taillandier Année de publication : 2011 Article en page(s) : pp 2818 - 2832 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] connaissance thématique
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] instance
[Termes IGN] méthode heuristique
[Vedettes matières IGN] GénéralisationMots-clés libres : Knowledge revision Problem solving Trial and error method Cartographic generalisation Résumé : (auteur) Humans frequently have to face complex problems. A classical approach to solve them is to search the solution by means of a trial and error method. This approach is often used with success by artificial systems. However, when facing highly complex problems, it becomes necessary to introduce control knowledge (heuristics) in order to limit the number of trials needed to find the optimal solution. Unfortunately, acquiring and maintaining such knowledge can be fastidious. In this paper, we propose an automatic knowledge revision approach for systems based on a trial and error method. Our approach allows to revise the knowledge off-line by means of experiments. It is based on the analysis of solved instances of the considered problem and on the exploration of the knowledge space. Indeed, we formulate the revision problem as a search problem: we search the knowledge set that maximises the performances of the system on a sample of problem instances. Our knowledge revision approach has been implemented for a real-world industrial application: automated cartographic generalisation, a complex task of the cartography domain. In this implementation, we demonstrate that our approach improves the quality of the knowledge and thus the performance of the system. Numéro de notice : A2011-581 Affiliation des auteurs : IGN+Ext (1940-2011) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asoc.2010.11.012 Date de publication en ligne : 27/11/2010 En ligne : http://dx.doi.org/10.1016/j.asoc.2010.11.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83482
in Applied soft computing > vol 11 n° 2 (March 2011) . - pp 2818 - 2832[article]
Titre : Using belief theory to diagnose control knowledge quality: Application to cartographic generalisation Type de document : Article/Communication Auteurs : Patrick Taillandier Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2009 Conférence : IEEE-RIVF 2009, International Conference on Computing and Communication Technologies 13/07/2009 17/07/2009 Danang Vietnam Proceedings IEEE Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] qualité des connaissances
[Termes IGN] qualité des données
[Termes IGN] théorie de Dempster-Shafer
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging. Numéro de notice : C2009-050 Affiliation des auteurs : COGIT+Ext (1988-2011) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/RIVF.2009.5174663 Date de publication en ligne : 28/07/2009 En ligne : https://doi.org/10.1109/RIVF.2009.5174663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99115 Documents numériques
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Using Belief Theory to Diagnose Control Knowledge Quality ... - pdfAdobe Acrobat PDF
contenu dans CSTST 2008, the 5th International conference on soft computing as transdisciplinary science and technology, October 28th - October 31st 2008, University of Cergy-Pontoise, France / Richard Chbeir (2008)
Titre : Knowledge revision in systems based on an informed tree search strategy: application to cartographic generalisation Type de document : Article/Communication Auteurs : Patrick Taillandier Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2008 Conférence : CSTST 2008, 5th International conference on soft computing as transdisciplinary science and technology 28/10/2008 31/10/2008 Cergy-Pontoise France Proceedings ACM Importance : pp. 273 - 278 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre (mathématique)
[Termes IGN] base de connaissances
[Termes IGN] découverte de connaissances
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] révision des connaissances
[Termes IGN] stratégie
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation. Numéro de notice : C2008-006 Affiliation des auteurs : COGIT+Ext (1988-2011) Thématique : GEOMATIQUE Nature : Communication DOI : 10.1145/1456223.1456281 En ligne : http://dx.doi.org/10.1145/1456223.1456281 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82644 Révision automatique des connaissances guidant l'exploration informée d'arbres d'états / Patrick Taillandier (2008)
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