Advice Provision for Choice Selection Processes with Ranked Options

Amos Azaria, Yakov Gal, Claudia V. Goldman, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Choice selection processes are a family of bilateral games of incomplete information in which a computer agent generates advice for a human user while considering the effect of the advice on the user’s behavior in future interactions. The human and the agent may share certain goals, but are essentially self-interested. This paper extends selection processes to settings in which the actions available to the human are ordered and thus the user may be influenced by the advice even though he doesn’t necessarily follow it exactly. In this work we also consider the case in which the user obtains some observation on the sate of the world. We propose several approaches to model human decision making in such settings. We incorporate these models into two optimization techniques for the agent advice provision strategy. In the first one the agent used a social utility approach which considered the benefits and costs for both agent and person when making suggestions. In the second approach we simplified the human model in order to allow modeling and solving the agent strategy as an MDP. In an empirical evaluation involving human users on AMT, we showed that the social utility approach significantly outperformed the MDP approach.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Place of PublicationQuébec Convention Center, Québec City, Québec, Canada
PublisherAAAI Press
Number of pages2
Publication statusPublished - 2014
EventTwenty-Eighth AAAI Conference on Artificial Intelligence - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014


ConferenceTwenty-Eighth AAAI Conference on Artificial Intelligence
CityQuebec City


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