Abstract
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 bidder. The framework leverages algorithms from query learning to elicit value information via bids as the auction progresses. The approach reduces the multi-agent elicitation problem to isolated, single-agent learning problems, with competitive equilibrium prices used to facilitate auction clearing even without complete learning.
Highlights
We describe a modular elicitation framework for iterative combinatorial auctions
The distinction is important because iterative auctions, unlike single-shot auctions, allow for coordinated preference elicitation coupled with price discovery
The prevalence of XOR in iterative combinatorial auctions is an artifact of the way they are often designed: an iterative auction can be interpreted as a dual method on a linear program for the allocation problem, and a linear programming formulation leads naturally to XOR representation of bids and prices [de Vries et al 2007; Parkes and Ungar 2000]
Summary
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 bidder. The framework leverages algorithms from query learning to elicit value information via bids as the auction progresses.
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