Advice Provision for Choice Selection Processes with Ranked Options

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: 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.

Keywords: Human modeling, advice provision, persuasion.

Citation: Amos Azaria, Sarit Kraus, Claudia Goldman and Kobi Gal. Advice Provision for Choice Selection Processes with Ranked Options.

Download: http://bit.ly/1U002VV

About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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