Methods for improving and updating the knowledge of a generalization system
Résumé
In this paper we present a method to improve and to update the knowledge used for the automation of the generalization of buildings based on agent paradigm. We propose to store 1/ each building decision, 2/ the reason why the decision was taken (the conflicts) 3/ the result of each algorithm (an improvement or not) and 4/ the successful process chain within all trials. At the end, the processes of all buildings are compared in order to identify the weakness (for example the case where a specific algorithm is often used but never succeeds). When a deficiency is identified we introduce new rules and we study the effect of this change on the efficiency of the process. It can be used either to improve existing knowledge or to introduce new rules associate to the use of a new measure or a new algorithm. The first study has been made on building independent generalization to set the learning methodology. We wish now to apply it on more complex cases such as contextual generalization which still needs knowledge improvement.
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