Efficient preference learning for AI-powered group decision-making
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Authors
Zhao, Zhibing
Issue Date
2020-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
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
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY