Abstract
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 language | English |
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Pages | 2411-2412 |
Number of pages | 2 |
State | Published - 2012 |
Event | 26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada Duration: 22 Jul 2012 → 26 Jul 2012 |
Conference
Conference | 26th AAAI Conference on Artificial Intelligence, AAAI 2012 |
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Country/Territory | Canada |
City | Toronto |
Period | 22/07/12 → 26/07/12 |
Bibliographical note
Publisher Copyright:Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.