Active preference learning using maximum regret

Nils Wilde, Dana Kulic, Stephen L. Smith

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

19 Citations (Scopus)


We study active preference learning as a frame-work for intuitively specifying the behaviour of autonomous robots. A user chooses the preferred behaviour from a set of alternatives, from which the robot learns the user's preferences, modeled as a parameterized cost function. Previous approaches present users with alternatives that minimize the uncertainty over the parameters of the cost function. However, different parameters might lead to the same optimal behaviour; as a consequence the solution space is more structured than the parameter space. We exploit this by proposing a query selection that greedily reduces the maximum error ratio over the solution space. In simulations we demonstrate that the proposed approach outperforms other state of the art techniques in both learning efficiency and ease of queries for the user. Finally, we show that evaluating the learning based on the similarities of solutions instead of the similarities of weights allows for better predictions for different scenarios.

Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)
EditorsHyunglae Lee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728162126
ISBN (Print)9781728162133
Publication statusPublished - 2020
EventIEEE/RSJ International Conference on Intelligent Robots and Systems 2020 - Virtual, Las Vegas, United States of America
Duration: 24 Jan 202124 Jan 2021 (Website)

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems 2020
Abbreviated titleIROS 2020
Country/TerritoryUnited States of America
CityLas Vegas
Internet address

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