Intelligent advice provisioning for repeated interaction

Priel Levy, David Sarne

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

17 Scopus citations


This paper studies two suboptimal advice provisioning methods ("advisors") as an alternative to providing optimal advice in repeated advising settings. Providing users with suboptimal advice has been reported to be highly advantageous whenever the optimal advice is non-intuitive, hence might not be accepted by the user. Alas, prior methods that rely on suboptimal advice generation were designed primarily for a single-shot advice provisioning setting, hence their performance in repeated settings is questionable. Our methods, on the other hand, are tailored to the repeated interaction case. Comprehensive evaluation of the proposed methods, involving hundreds of human participants, reveals that both methods meet their primary design goal (either an increased user profit or an increased user satisfaction from the advisor), while performing at least as good with the alternative goal, compared to having people perform with: (a) no advisor at all; (b) an advisor providing the theoretic-optimal advice; and (c) an effective suboptimal-Advice-based advisor designed for the non-repeated variant of our experimental framework.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Number of pages8
ISBN (Electronic)9781577357605
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016


Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence ( All rights reserved.


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