The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/b6e1f929-675e-4867-b657-a6c77d9f67ae

Liscio, Enrico; Cavalcante Siebert, Luciano; C.M. (Catholijn) Jonker; Murukannaiah, Pradeep K. (2023): Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems - code. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/b6e1f929-675e-4867-b657-a6c77d9f67ae.v1

Software

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Active Learning, natural language processing, NLP, Value estimation

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Enrico Liscio , Luciano Cavalcante Siebert , C.M. (Catholijn) Jonker , Pradeep K. Murukannaiah

Code for the paper "Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems", under review at JAIR. This code contains five value preferences estimation methods based on participants' choices and textual justifications for a survey. Further, it implements an Active Learning pipeline with three sampling strategies---random, uncertainty, and driven by the estimation of value preferences. The pre-trained Dutch mode RobBERT is used to perform value classification.

• 2023-12-18 first online, published, posted

4TU.ResearchData

python files

• Hybrid Intelligence Center (a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research).

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems