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.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||8|
|State||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 12 Feb 2016 → 17 Feb 2016
|Name||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Conference||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||12/02/16 → 17/02/16|
Bibliographical noteFunding Information:
This research was partially supported by the Israel Science Foundation (grant No. 1083/13) and the ISFNSFC joint research program (grant No. 2240/15).
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