Efficient preference learning for AI-powered group decision-making

Authors
Zhao, Zhibing
ORCID
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Other Contributors
Xia, Lirong
Zaki, Mohammed J., 1971-
Magdon-Ismail, Malik
Pazour, Jennifer A.
Issue Date
2020-05
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
A modern group decision-making paradigm involves three phases: data collection, preference learning, and preference aggregation. All three phases can be very challenging due to the large number of alternatives and resource constraints. How can we actively collect the most informative data with low costs? How can we learn from various structures of preferences efficiently and predict unknown preferences? When can we confidently explain the learning outcome? How can we make a group decision from uncertain predictions? This dissertation aims at answering these questions.
Description
May 2020
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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