On the basis of the current development of active preference learning, we are trying to extend the algorithm in such a way that can exploit more information from users from asking suitable questions to modifying available algorithm in such a way that helps to find the optimal set of parameter faster than the currently available algorithm. The designed algorithm is then applied to the work of RIENTR@ project for validation.
Ongoing work: Study of improving Active Preference Learning and application on the context of immersive wheelchair simulator
Le Anh Dao;Matteo Malosio
2021
Abstract
On the basis of the current development of active preference learning, we are trying to extend the algorithm in such a way that can exploit more information from users from asking suitable questions to modifying available algorithm in such a way that helps to find the optimal set of parameter faster than the currently available algorithm. The designed algorithm is then applied to the work of RIENTR@ project for validation.File in questo prodotto:
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