Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Incentive Decision Processes
Sashank Reddi, Emma Brunskill
Abstract:
We consider Incentive Decision Processes, where a principal seeks to reduce its costs due to another agent's behavior, by offering incentives to the agent for alternate behavior. We focus on the case where a principal interacts with a greedy agent whose preferences are hidden and static. Though IDPs can be directly modeled as partially observable Markov decision processes (POMDP), we show that it is possible to directly reduce or approximate the IDP as a polynomially-sized MDP: when this representation is approximate, we prove the resulting policy is boundedly-optimal for the original IDP. Our empirical simulations demonstrate the performance benefit of our algorithms over simpler approaches, and also demonstrate that our approximate representation results in a significantly faster algorithm whose performance is extremely close to the optimal policy for the original IDP.
Keywords:
Pages: 418-427
PS Link:
PDF Link: /papers/12/p418-reddi.pdf
BibTex:
@INPROCEEDINGS{Reddi12,
AUTHOR = "Sashank Reddi and Emma Brunskill",
TITLE = "Incentive Decision Processes",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "418--427"
}


hosted by DSL   •   site info   •   help