Strategic Advice Provision in Repeated Human-Agent Interactions

Amos Azaria, Zinovi Rabinovich, Sarit Kraus, Claudia V. Goldman, Ya'akov Gal

Research output: Contribution to conferencePaperpeer-review


This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real world applications such as route selection systems and office assistants. To succeed in such settings agents must reason about how their actions in the present influence people's future actions. The paper describes several possible models of human behavior that were inspired by behavioral economic theories of people's play in repeated interactions. These models were incorporated into several agent designs to repeatedly generate offers to people playing the game. These agents were evaluated in extensive empirical investigations including hundreds of subjects that interacted with computers in different choice selections processes. The results revealed that an agent that combined a hyperbolic discounting model of human behavior with a social utility function was able to outperform alternative agent designs. We show that this approach was able to generalize to new people as well as choice selection processes that were not used for training. Our results demonstrate that combining computational approaches with behavioral economics models of people in repeated interactions facilitates the design of advice provision strategies for a large class of real-world settings.

Original languageEnglish
Number of pages2
StatePublished - 1 Jan 2012
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012


Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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