| Abstract (show) |
| Short Description |
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Mechanism design addresses the problem of designing rules for multiagent interactions when agents are self-interested and their preferences are unknown to the designer. We present an overview of automated mechanism design, a paradign of designing original mechanisms computationally given arbitrary design goals (objectives) and constraints. The techniques range from a linear (integer) programming formulation for finite problems, to stochastic search techniques for constrained mechanism design on continuous design spaces, with many applications to the design of voting protocols, auctions, and supply-chain simulations. |
| Target Audience |
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Our target audience is the AI agents researchers with interests in multi-agent systems, optimization under uncertainty, and electronic commerce, with little background knowledge required beyond basic knowledge of optimization (e.g., linear programming) and local search (e.g., simulated annealing). Multi-agent systems researchers could gain an understanding of classical mechanism design techniques as they may apply to problems in designing protocols for self-interested agents, as well as exposure to the new field of automated mechanism design, which allows automation of the design process in systems with highly specialized structure and objectives. Those interested in optimization under uncertainty will be exposed to novel areas of application for stochastic optimization methods. Finally, electronic commerce researchers could observe the effectiveness of a blend of game theory, optimization, and stochastic search in automatically designing voting protocols, auctions, and supply-chains. |
| Detailed Outline |
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Tutorial
outline.
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Presenters
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| Vincent Conitzer
is an Assistant Professor of Computer Science and Economics at Duke
University. He received Ph.D. (2006) and M.S. (2003) degrees in Computer
Science from Carnegie Mellon University, and an A.B. (2001) degree in
Applied Mathematics from Harvard University. His research focuses on
computational aspects of microeconomics, in particular game theory,
mechanism design, voting/social choice, and auctions. This work uses
techniques from, and includes applications to, artificial intelligence and
multiagent systems. Conitzer received an Alfred P. Sloan Research
Fellowship (2008), an Honorable Mention for the 2007 ACM Doctoral
Dissertation Award, the 2006 IFAAMAS Victor Lesser Distinguished
Dissertation Award, the AAMAS Best Program Committee Member Award (2006),
and an IBM Ph.D. Fellowship (2005). He is a co-author on papers that
received a AAAI-08 Outstanding Paper Award and the AAMAS-08 Pragnesh Jay
Modi Best Student Paper Award.
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| Yevgeniy Vorobeychik is a post-doctoral research associate at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on simulation-based game theory and mechanism design, algorithmic game theory, network economics, and machine learning. Dr. Vorobeychik was nominated for the 2008 ACM Doctoral Dissertation Award. He was also a recepiet of a STIET doctoral fellowship at the University of Michigan, as well as a distinguished Computer Engineering undergraduate award at Northwestern University. |