Y Vorobeychik, DM Reeves, and MP Wellman
Autonomous Agents and Multiagent Systems, 25(2): 313–351, 2012.
Copyright © 2011 Vorobeychik et al.
Abstract
We present a functional framework for automated Bayesian and worst-case mechanism design, based on a two-stage game model of strategic interaction between the designer and the mechanism participants. At the core of our framework is a black-box optimization algorithm which guides the process of evaluating candidate mechanisms. We apply the approach to several classes of two-player infinite games of incomplete information, producing optimal or nearly optimal mechanisms using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we provide evidence that ours is a promising approach to parametrized mechanism design for infinite Bayesian games.
Revised and extended version of a paper originally presented at UAI-07: Twenty-Third Conference on Uncertainty in Artificial Intelligence, pages 400-407, July 2007.
