Value-Based Policy Teaching with Active Indirect Elicitation

Value-Based Policy Teaching with Active Indirect Elicitation

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Value-Based Policy Teaching with Active Indirect Elicitation

dc.contributor.author	Zhang, Haoqi
dc.contributor.author	Parkes, David C.
dc.date.accessioned	2010-05-03T14:10:54Z
dc.date.issued	2008
dc.identifier.citation	Zhang, Haoqi and David C. Parkes. 2008. Value-based policy teaching with active indirect elicitation. In Proceedings of the Twenty-third AAAI Conference on Artificial Intelligence and the Twentieth Innovative Applications of Artificial Intelligence Conference: July 13-17, 2008, Chicago, Illinois, ed. American Association for Artificial Intelligence, 208-214. Menlo Park, Calif.: AAAI Press.	en_US
dc.identifier.isbn	978-1-57735-368-3	en_US
dc.identifier.uri	http://nrs.harvard.edu/urn-3:HUL.InstRepos:4039771
dc.description.abstract	Many situations arise in which an interested party's utility is dependent on the actions of an agent; e.g., a teacher is interested in a student learning effectively and a firm is interested in a consumer's behavior. We consider an environment in which the interested party can provide incentives to affect the agent's actions but cannot otherwise enforce actions. In value-based policy teaching, we situate this within the framework of sequential decision tasks modeled by Markov Decision Processes, and seek to associate limited rewards with states that induce the agent to follow a policy that maximizes the total expected value of the interested party. We show value-based policy teaching is NP-hard and provide a mixed integer program formulation. Focusing in particular on environments in which the agent's reward is unknown to the interested party, we provide a method for active indirect elicitation wherein the agent's reward function is inferred from observations about its response to incentives. Experimental results suggest that we can generally find the optimal incentive provision in a small number of elicitation rounds.	en_US
dc.description.sponsorship	Engineering and Applied Sciences	en_US
dc.language.iso	en_US	en_US
dc.publisher	Association for the Advancement of Artificial Intelligence	en_US
dc.relation.isversionof	http://portal.acm.org/citation.cfm?id=1620030	en_US
dc.relation.hasversion	http://www.eecs.harvard.edu/econcs/pubs/zp-aaai08.pdf	en_US
dash.license	LAA
dc.title	Value-Based Policy Teaching with Active Indirect Elicitation	en_US
dc.type	Monograph or Book	en_US
dc.description.version	Accepted Manuscript	en_US
dash.depositing.author	Parkes, David C.
dc.date.available	2010-05-03T14:10:54Z