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A Modular Framework for Iterative Combinatorial Auctions
(Association for Computing Machinery, 2008)
We describe a modular elicitation framework for iterative combinatorial auctions. The framework includes proxy agents, each of which can adopt an individualized bidding language to represent partial value information of a ...
Applying Learning Algorithms to Preference Elicitation
(Association for Computing Machinery, 2004)
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis ...
ICE: An Iterative Combinatorial Exchange
(Association for Computing Machinery, 2005)
We present the first design for a fully expressive iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language that is concise and expressive for CEs. Bidders specify lower and upper ...
Policy Teaching Through Reward Function Learning
(Association for Computing Machinery, 2009)
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent's decisions by providing limited incentives. In this paper, we consider the specific objective of inducing ...